Title:مقدمه ای بر شاخص مقرون به صرفه بودن HT و کاربردها در برنامه ریزی

۲۰۱۱-۰۶-۲۴ ارائه دهندگان: ماریا چوکا اوربان، استفانی مورس و پیتر هاس این وب‌کست فقط برای مشاهده در دسترس است، برای اعتبارات AICP CM قابل استفاده نیست. این وبینار شاخص H+T را معرفی می‌کند، ابزاری که توسط مرکز فناوری همسایگی توسعه یافته و به روشن کردن هزینه‌های پنهان توسعه ناپایدار کمک می‌کند. شاخص H+T هزینه‌های حمل و نقل مرتبط با موقعیت یک خانه را برای ۳۳۷ کلان شهر ایالات متحده با قیمت‌گذاری مبادله بین هزینه‌های مسکن و حمل‌ونقل که خریداران و اجاره‌کنندگان هنگام انتخاب محل زندگی انجام می‌دهند، مستند می‌کند. جوامع جمع و جور، قابل پیاده‌روی، با کاربری مختلط با دسترسی راحت به حمل و نقل عمومی و مراکز اشتغال، به دلیل هزینه‌های مسکن بالاتر، گاهی گران به نظر می‌رسند – اما شاخص H+T نشان می‌دهد که این مکان‌ها اغلب می‌توانند زندگی مقرون به صرفه‌تری نسبت به جوامع شهری با تراکم پایین‌تر داشته باشند. زیرا خانوارها می توانند خودروهای کمتری داشته باشند و همچنان کیفیت زندگی بالایی داشته باشند. شاخص H+T علاوه بر ارائه داده‌های هزینه خانوار در سطح گروه بلوک سرشماری، تخمین مایل‌های پیموده شده توسط وسایل نقلیه خانگی، دسترسی به حمل‌ونقل، نزدیکی به مشاغل و انتشار گازهای گلخانه‌ای خانگی از رانندگی را نیز ارائه می‌کند. نحوه عملکرد Index را با توضیح دقیق و یک نسخه نمایشی زنده بیاموزید، بشنوید که چگونه برخی از جوامع از ایندکس برای اهداف برنامه ریزی استفاده می کنند، و در مورد به روز رسانی های آتی پایگاه داده H+T Index که راه های استفاده از آن را گسترش می دهد، بیاموزید.

قسمتي از متن فيلم: So much things disappear but a handful where are they all going what the broadcast is now starting all attendees are in listen-only mode hello my name is Brittany kavinsky and I just want to welcome everyone it is now 1 o’clocks that we will begin our presentation

Shortly today I’m jun 24th we will have our presentation on the HMT affordability index and applications and planning for help during today’s webcast please feel free to type your questions in the chat box found in the webinar tool bar to the right of your screen or call one eight hundred 263 6317 for

Content questions please feel free to type those in the questions box and we will be able to answer those at the end of the presentation during the question-and-answer session here’s the list of the sponsoring chapters division and universities I would like to thank all of the participating chapters divisions and universities for making

These webcasts possible as you can see we have a few upcoming webcasts I in the month of july august in September to register for these upcoming webcasts please visit www webcast HTM and register for your webcast of choice to log your cm credits for attending today’s webcast please go to ww plng org

Slash CM select today’s date jun 24 and then select today’s webcast the agency affordability index and applications and planning this webcast is available for one and a half cm credits we are recording today’s webcast and it will be available along with a stick slide per page PDF of the presentation at wwt APA

Org slash webcast archive this time I would like to introduce our speakers for today Maria Chaka urban Peter hast and Stephanie Morse as Maria as TNT’s director of Transportation and Community Development Maria Chaka urban handles land use transportation housing and economic development issues she works with state and local government

Officials as well as civic groups and other constituents to advance Center for neighborhood technology initiatives in these areas in this capacity she advances the application of CNT tools in the development of plans for more sustainable development in urban areas among the tools that CNT has developed are the housing and transportation

Affordability index a planning tool that documents the combined cost of housing and transportation by neighborhood within urban regions the cargo and transit oriented development optimizer tool and the National Tod database a one-stop shop that aggregates census transportation planning package local employment dynamics and HMT data to the half mile area around every fixed

Guideway transit station in the US with the aim of accelerating the adoption of Tod and a greater number of places since joinings DNT in 1994 dr. Peter Haas has revolutionized the geographic analysis of social environmental and economic data to produce groundbreaking tools for measuring sustainability in urban areas

Dr. Haas has been integral in the development of cnts location efficiency metrics and developed its housing and transportation affordability index co-produced with the Brookings Institution and he acted as the analytical director for the 22-member CNT research team producing a greenhouse gas emissions inventory and 33 mitigation strategies for the Chicago

Climate action plan his work in GIS web development and data analysis have come together into the geographical research and information department at CNT where dr. Haas and his colleagues work to provide technical geographical and analytical input to all of sea antes programs prior to CNT dr. Haas was a

Systems Manager Steven energy associates adjunct faculty at the william rainey for college and postdoctoral researcher at Cornell’s university laboratory for nuclear studies and the firm I National Accelerator Laboratory dr. Haas has a PhD in particle physics from The Ohio State University Stephanie Moore’s research coordinator is a key analyst

For community projects utilizing CMT’s housing and transportation affordability index a groundbreaking new tool measuring the effect of urban form and location efficiency on household affordability Stephanie focuses on coordinating and assisting with data analysis as well as a development and expansion of the agent II index prior to joining CNT Stephanie relocated to

Chicago to pursue her interest in sustainable development after receiving her master’s degree in natural resources from the University of Vermont while the graduate student she also earned a certificate ecological economics from the gund Institute of ecological economics housed at the University of Vermont good afternoon to all of you it’s our

Pleasure here at sandys to take part in this 88 sponsored webcast on the h-60 affordability index i’d like to thank jennifer kelly and her assistants 30 price and brittany kubinski for organizing the webcast and i’d also like to thank second e show on that fork here at CNT for making this possible I’m

Maria choker urban the director of Transportation Community Development and as Jennifer just said we have peter has and Stephanie Morse joining me today as to make this presentation today’s presentation is going to cover what the index is why we created it and what are the key insights that it gives us and

Peter’s going to talk about current efforts to update the HC index in the next few months then we’re going to cover different ways that the index has been used and Stephanie will be giving us an introductory demonstration of the H+ T website we’ve left time at the end

Of our presentation for questions if you have questions during the presentation we encourage you to submit them using the chat tab on the side of your screen will organize the questions eliminate duplicates and with any luck we’ll get to all of them before work through today and no presentation would be complete

Without a commercial advertisement so this is my plug for the Center for neighborhood technology we’re a 33 year old not for profit based in Chicago it operates nationally we’ve been dedicated to urban sustainability because before the term came into vogue CNT thinks of itself as a think and do tank it

Combines research and tool development with demonstration projects to prove and test our hypotheses the results of all of these efforts feed into our advocate see in policy development works we have four program areas covering energy climate transportation and water and our research and gif division underpins all

Four of those program areas and we have an unparalleled staff and our research divisions that I really want to acknowledge I’m going to turn it over to peek who’s going to talk about why she and she goes to school and how it works all right Thank You areum so I’m just

Going to start at the first principle tee-rrific and um so what is affordability and how is its fine in our country right well well pretty much the conventional standard right now is that your housing costs the household housing costs should be about thirty percent of of its income and that that rule of

Thumb has really started driven the development patterns across the u.s. lack of knowledge of how much it costs to actually get around at those places is as it has been deficient we think in that in that standard and and really has forced us to make him in decisions with

An incomplete set of information about about a certain location that we’re going to now we’re going to choose to live in or we’re going to choose to develop it for developers or investors so the sum that up the rule of thumb fuels the search for the lowest cost

Land to build housing on regardless of the location so as a result home seekers as they say drive to you qualify for a mortgage or even a lease to look for some rental areas and and and that may not serve all their purposes so that’s that’s at the household level of

Decision making but more importantly residential lending and construction industry as I produced an enormous amount of housing stock in location inefficient neighborhoods because lands cheap and it’s easy to develop and it seems like they’re building affordable homes when they’re doing that for it for the market so in other words good thirty

Percent rule of thumb and it’s more than a roll them since it drives lending and rental decisions is incomplete because it does not consider all of the information related to a specific location so we promote and an advocate for looking at a broader definition of what affordability means

That includes the cost of transportation so we might say well why transportation why not healthcare or something else but because well print editions as planners you all know that it’s uh it’s very connected first of all to place ask anybody what they think do people at the

Edge of urban areas spend more or less on transportation then the people who live in the urban center and people will say duh of course of course the urban dwellers send a lot less so so we want to we want to make that con that we can

Take advantage of that and put numbers to that we know that it’s the second largest also expensive sure after housing and the costs are largely unknown as someone as the same person how much you spend on transportation and they’re likely to maybe tell you how gas

Prices are or what a new car might cost but but to really put it down in a monthly kind of budget way would challenge anybody so people make in it and that’s just where you live now I’ll but to think about when you’re moving or where you’re going to develop some

Housing how would you know what people are sending in that area and that’s really what we’re driving this index towards so we’re driving people to invest in a location that that on well the current the current definition of affordability is driving people to to invest in locations where they may be

Spending a lot more in transportation and therefore exposing themselves to the volatile class of transportation and so a lack of affordable information fuels sprawl and inefficient development patterns so how we do this is we use a calculated transportation costs and compile them at at with measures are combined them with measured housing

Costs at a neighborhood level to provide a more complete picture of what of what real affordability of a being so let’s just think a little bit about the factors that drive that drive transportation costs well we know from the the Consumer Expenditure survey that American households on average across

The whole country spend about eighteen percent of their of their expenditures on transportation so that’s not really eighteen percent of their saving our income depending on how much they’re saving worth their going into debt and so on but but we know of all the outlays it’s about eighteen percent of what they

Spend but what we do also know is that when you look inside metropolitan areas within the country places like Baltimore been as low as thirteen percent Chicago something around fifteen percent but then on the other end second places like Detroit spending up more than eighteen percent earning nineteen twenty percent

So so so we very right a transportation costs vary on where you live and again go back to that does statement but looking at the transportation costs that a metropolitan level decision good enough and doesn’t really focus the decision-making the way that we think it should so what are so we’re looking at

Is to look at the neighborhood level and what we’re suggesting as a benchmark that would say ready given neighborhood we would say that neighborhoods affordable for a household if they’re sending something like forty five percent will not something like that’s what we’re studying standard forty five percent of their income on housing and

Transportation and once you do that once you redefine what that is you no longer using that thirty percent for housing and fifteen percent for transportation but you could think of get imagine the scenario where someone could find a house that that would in traditional sense be not affordable where it might

Be thirty-eight percent of their income but then if their transportation costs are only seven percent if it’s a very dense urban place Thank Manhattan or you know Chicago or so Francisco places like that where housing costs can be high but transportation costs can mitigate that that still makes for an affordable

Neighborhood so ideally we’d have lots of places in the country to choose from to achieve this balance and one thing our research our research has shown that in the u.s. there are quite a few places but we really believe there could be more and I think good primer like you

All can talk with people so so what does some of the analysis show well so what makes sense what makes them for a location efficient neighborhood so as I said you know we look across from the metropolitan areas we see this variation but we know that in Baltimore there’s

Got to be places that people are spending more than just thirteen percent on their on their transportation costs and then Detroit those places where it’s lower where where they live and people are living in much more efficient places so so we sort of zoned in that as to

Look at at areas where where you really see good access to services walkable destinations good good access to transit and jobs and and you know all of this is sort of enabled by aha high moderate density of housing um you notice i didn’t put land-use mixing in here and

Probably should look at land you quickly thinking about what makes it were an efficient neighborhood but i’ll talk about that a little bit a little bit later about what what are some of our researchers shown on that so what we what we know is that places that have

These asked patra braids we call have high location efficiency or our location efficient places and i just wanted to talk about that term we’ve been throwing it around quite a bit so so what we mean by that is it’s much like energy efficient building don’t keep you warm

But without trying and being just as warm as everybody else you’re saving money on gas electricity because your house is energy-efficient so so likewise allocations efficient neighborhoods allows you to do all the things you want get your kids to school go to soccer practice get to the grocery store and

And all things you do in a day to work but thank you transportation dollars because you don’t have to you don’t have to depend on on on high-cost amenities to do that like bottles and let’s say so in other words it saves you transportation dollars without having to

Compromise so as a result households as places that have have good attributes in this list what we find own less than an auto per household will drive a lot less and and and use more public transportation but also and it’s not included in here because it physically come on an

Explicit cost but people will want more and drive more and in generally federal on some research out of the University of British Columbia among others have shown that it really adds to the health health benefits as well so so in order to drill down to a neighborhood level

And look at these variables I we fill up a model which I’m going to go and do a little bit of detail here and so so the idea here is is that the dates will see you right so you get housing costs those data are fairly widespread and on what

We do is we go to the census and we say we collect the data on selected monthly ownership costs for owners and gross rent for renters and that and that’s what we call as our housing part of it but since we don’t have transportation measures down at this level we we have

Developed a mop so the idea is you come out with your transportation costs that’s that’s what we’re trying to get to and what we what we have developed is a model that predicts three three three types of behavior out of a household how many cards or thought households own how

Far they’ll drive those cars and then how much they’ll use public trans transit and then end of course we have to apply a cost factor for that and I might just say that so this little arrow here represents you know just a multiplication of a class factor and

Just to say what what we’re using it’s about five thousand dollars in two thousand dollars to own a car whether you drive it or not about nine cents again in two thousand dollars just to drive that every mile and then um and some transit costs that what we do there

Is we reallocate the totals transit fares that are collected by lb agencies to the households that are served by those agencies that you stranded so we in the words of a statistician might think normalized the amount of dollars spent by every household such that the total equals the amount collected by the

Transit agency well that’s about as complicated as I’ll try to get today these guys are all know that I can give a little bit scary to a in a language that maybe not a rating for City anyway so so but the main part of our model is really vicious is how these

Nine variables on the left of this slide connect to those three behaviors auto ownership usage and enter and accuses what we’ve done is broken down into two different classifications of variable ones that are basically play spades and I’ll just list what they are and you can

See how well is this sort of correlates with those with the previous slide out of the type of the image we have so residential or postvention density is a measure of households the residential is helpful to residential acre where we where we estimate that gross density is

Just a hustle total acre whether it’s a park or an industrial area in the area it just gets counted the same I’m the average block size is a measure of we use that serve the surrogate for walkability we know that people will walk when they’re smaller blocks what we

Call our transit conductivity in X and it’s kind of a complicated gis-based index that we develop that really really how come how easy is it is it to get to a bus now where that bus goes or a train but where it goes and I just would

Suggest that if you want to know a little bit more detail where I where I went on a little bit more about all of this left last year in Chicago we have tuesdays at the APA and last July I gave a seminar like that and you can go to VA

Pas planning that organ and pick up that website and you can see more than you probably want to know about these wet stuff so so spices to say the transit connectivity and the job density is really a sort of complicated indices to say how much jobs in transit are around

And we had to you we used a verge time journey to work because if we thought it would be important but it turns out it’s actually not that important we know that only twenty percent of trips and the day are getting to work most everything is doing everything else most of most of

People’s transportation class goes to go into the grocery store and piano lessons and things like that so then so that’s that’s the location but then we know that household variables drive with you know wealthy households Olmo present and lower-income households in bigger hassle but more and the more workers for household

Outside of the house the people who work outside of the household week we see that thats related to Susie behaviors as well so we fit for all of all nine of these variables come up with a set of complicated regression analysis equations that we can then predict that

Given these variables but then the real crux of the matter in order to make an index out of this is we hold these household variables constant and we do that so that you can really focus on the the inherent location efficiency of every neighborhood so in other words we’re controlling for local demographics

So that our index measures into measure of affordability inherent to the neighborhood because its development patterns and not who happens to live in that neighborhood at any given time now I said I’d mentioned land use so you notice that there is no land use in here

But we have done some research and work first of all land use is really hard to catch there’s not a ubiquitous apply a gif layer of all land use in the state so where we have focused in and Washington DC in and in bay area what we

See is that yeah it does correlate highly with these behaviors but by the time we do get fit for these six other neighborhood variables land use does improve the fit but it but the fact that it’s so hard to get we do not put it in

The model on it but in incremental in its improvement so we think we’re capturing most of what land use the benefits of land use with me six variables okay so so how does this look in the real world well I just show you example and Stephanie Kings will show

You how to how to access please enter on our website and and and and really get into it but let me just sort of give you a quick brush of what you might see so this is the st. Louis Missouri and Illinois area single is right there

Yellow on is math means that them mouthing stock within that is less than thirty percent of the typical household in the in the st. louis metro area which i just said what the typical household it makes about 44 k to nap people house and a little bit more than a worker per

Household so so a lot of the area is affordable in fact i’m i think eighty percent of the housing stock in the st. louis metro area is it is represented in the yellow black groups in that in that map now alternatively when we were given from a our index the picture changes somewhere

So it’s the same household but now we estimated with it what that household would spend on transportation and then folded it into the equation and require that it’s less than forty-five percent you see that the area of affordability really shrinks and is focused around the urban core but also in some small towns

And and some areas that are efficient not necessarily within the city um just for just for completeness about the yellow area here represents a little less than half around forty six percent of all the housings Backman singles natural area but I want to bring one

More one more map to talk about what it is affordable housing further for the most stretched households and what this map shows is there is a recalculation of affordable or what transportation costs are for a moderate helpful someone’s making about eighty percent of the regional ass median income somewhere

Around 35 and a half ka year same thing sighs and workers for household used to be that now now though the area of affordability has really shrunk and and limited but so there’s opportunity in that limited area where where there is dense urban form good transportation infrastructure and so affordable housing

Should probably be focused in that area so couple things I mean one of the things you look at any name and look at any metro area in the whole United States you basically see a very similar story that gets poked and kept me I’ll

Show you guys how to do that and if you haven’t already played around with it with the the website HDA index that org but so I’m going to change a little bit here and just say that you know everything you’ve been looking at here is based upon two thousand senses what

Kind of a date not even kind out it is fairly odd to take but so we’re now in the process of updating our our our data and I on our website and so this summer will over well we’ve been working on it for a little while but here’s a little

Preview of what we’re going to release at the end of the summer so first of all we’re going to extend the coverage octo now we did the 337 metropolitan areas as defined by the two thousand senses and now we’re going to be looking at 940 what are called metropolitan

Micropolitan area is I’m sure annoyed with those terms you’re not ask a question so that will battle we were covering about eighty percent of the population and now we’re going to be upwards of ninety percent we’re using the ACS 5-year estimates which is a rolling estimate which it has the

Advantages it was just works well in it well we believe it will we could recalibrate and be more current than keep a rolling average of that but this is the first roll out of using these data since a bit of a challenge as anybody who’s used any of the hds data

Probably knows we’re developing a better national transit database I talked about that transit conductivity in next well when we built the site that you said work that is often available now we really didn’t have transit data for all metropolitan areas and for a lot and

Most of them but we didn’t have them for all so now we’re developing a national transit database so we really can look kapil’s to apples comparisons of metro region to metro region the other the other detail is a bit of a noun getting a little bit down in the weeds here but

Remember I talked about the five thousand dollars for a car and that’s just one average we use everywhere and people have sort of suggested that may be inadequate in some ways that people people various income cohorts may spend more or less on on cars ownership so we’re developing a better and more

Details on version of that we’ve been talking to the department US Department of Transportation and they’re interested in pursuing this as well so we’re sort of looking to see we haven’t got a solution to this but we we will have a better more detailed ownership model win

Maybe not one we roll it out initially but as it goes forward and one of the one other thing that people have mentioned is that our are you are measure of walkability and I mentioned back when I talk about the parameters that we use is black side and people

Said you know what we really use an intersection density we think that’s better other people use old black densities better and so so we’re looking at different measures and seeing what’s going what’s going to work the best and that’s really what Rises so really excited we’re going to we’re going to inhale

Website even more than stephannie shows now they were really excited to see what we what we see is and in ways that people will use this new and creative data and I just want to say a little bit of a preliminary result is that basically it looks the same things have

Changed but but the trends are all all the things so now that you have a little background on the agency index I’ll turn it back through Maria and who will present some of the ways planners policymakers and others have you thanks Pete and what peak didn’t say is that we

Didn’t originally answer Davis for 337 metro areas we started small and the index has grown over time really from just a handful of Metro regions for the current coverage and as he said we’re poised to grow again to capture micropolitan areas across the country the fact of the matter is that the

Growth in the coverage of the index has led to more usage and what I’d like to do today is talk to you about some of the places and uses to which the index has been put at today’s audience for planners representing npos regional councils local county state and federal governments and consultants really

Represents a sweet spot of index users the CMT is trying to reach which the h plus C index but having said that I want to point out that another key audience for the index is the general public I imagine that all of you reach a point in your jobs where you’re trying to

Convince people whether they’re appointed or elected officials business people resident or advocates in the housing and transportation arenas of the wisdom of your proposals for more sustainable forms of development my experience has been that the index is a very powerful tool for getting people to understand the notion of sustainability

And what it involves a very basic level people know that for their to be sustainable they’ve got to be affordable and because the index talks about housing and transportation costs at a household level and they very quickly understand and grasp the impact on their pocketbook and kind of having

Reached an understanding for themselves when they see regional maps for municipal maps of housing and transportation affordability it’s a pretty small jump for them to understand the implications for their own communities and regions so I think a point I definitely want to leave you with today is that the index resonates

With people and makes it easier to reach consensus on future development I’m going to cover a range of age 50 applications today yes include regional applications the siting of affordable housing transit planning and scenario evaluation just to name a few I urge you as you hear the presentation if you have

Questions please pass them through but don’t focus just on the contents of the presentation I want you to pay attention as well to the various devices that CNT uses to get our messages across because they are very effective from our experience so the first application is going to be a regional planning

Application one of the regions in the country that’s done a lot with h plus B is the San Francisco Bay Area the Metropolitan Transportation Commission their mpo hired CMT to develop a custom analysis of housing and transportation affordability for their region and to recommend policy reforms that would lead

To improve affordability as a way of advancing the state of practice there and so we opened our analysis with the standard shot that Pete showed you before and this is a little bit different view than you would get from the website because we were doing a custom analysis for the MPO

And so it’s showing more gradations of affordability but still the point is that in the yellow and green areas you can have housing affordability looking at the map on the left and when you transition from just housing clock the housing and transportation class on the right you see that the area of

Affordability shrink considerably within the region these two views both constants for median income and are directly comparable to each other this view changes the underlying assumptions a little bit in that it calculates affordability for households earning eighty percent or less of area median income and you can see from this view

That there’s really very few places in the bay area with average housing and transportation costs that are affordable to families that in from level maps are great for showing how affordability plays out geographically but they give no sense of the order of magnitude of the numbers behind them and as a result

One device with CNC uses a lot is to pair maps with bar charts to give people a second way to gauge the loss of affordability this chart shows that while the loss of affordability is striking for households that burn area median income I when you shift from

Housing a thirty percent of income to housing and transportation costs at forty five percent of income the lots in affordability is dramatic across all three scales for low moderate and low income people and it’s particularly acute for those earning eighty percent or less or more fifty percent or less of

Income another device that CNT often resorts to is showing a strictly transportation cost for a region in this case we nap out against the Bay Area Transit System and the point of this presentation is that transit in and of itself on is not sufficient for lower transportation

Class you can see that the transit system whines its way out to very remote parts of the region but in many of those places on transportation costs fall into the pale blue level which is twelve thousand or more dollars twelve thousand or more of a year up to 14,000 so what’s

Really needed is the density the smaller block size the mix of uses in the job accessibility that p preferred to in order to achieve affordability in the interest of time I’m showing you only a few of the maps and analyses that we prepared for MTC but I want to close

This little chess or by saying that three tangible things resulted from this work the first is that there are Metropolitan Transportation Commission set a goal of reducing housing and transportation burden for low and moderate income residents like ten percent as part of their 2035 regional transportation plan they’ve also adopted

The HC index along with others sister agencies like the association of Bay Area governments as a performance measure under the sustainable communities strategy that’s required by SB 375 in California and finally most recently this year the MTO have allocated ten million dollars in seed capital for a transit-oriented development land acquisition fund that

They expect to grow to 40 million dollars with private matches so that the region can add to the supply of affordable housing along their transit corridors thereby ensuring that people at all income levels will have access to affordable transfer the second application demonstrates how the index can be used to optimize the

Siting of affordable housing so that it does come with affordable transportation CMT undertook this analysis extra Illinois became the first state to adopt the index in making housing transportation and economic development decisions we’ve studied eight years worth of data related to housing development finance are the Illinois housing development authority from 2001

To 2008 the determine whether policies put in place in 2005 have you had a measurable impact I’m the combined cost of housing and transportation affordability since we’re talking about developments that have been financed by the State Housing Finance Agency by definition the housing is affordable so

What we’ve done here is map the IDA finance development against transportation costs and if you look at the map the transportation costs are lowest in city of Chicago and they rise in concentric arcs around the city if you move from Chicago to less than suburban and exurban areas the upper

Right-hand chart is important because it tells us that itís policies that have had an effect on preserving lower transportation costs for the residents of their development and you see that the average transportation cost is a full percentage point lower for the neighborhoods where I de developments are located than they are for the

Regional average and then the lower chart I shows similar information broken out by geographies that are readily accepted and understood in the Chicago area and those are Chicago its inner ring suburbs and the ex urban areas just to demonstrate how leaf transportation costs vary across the region for

Idle developments we also cut the data by whether the IDA development said access to transit and this parts particularly powerful because it shows that residents can save as much as three thousand dollars a year by living in the development that has access to both lesson train server rather than one that

Has neither type of service and next three thousand dollars represents a pretty significant savings at eighty percent of median income which is forty one thousand dollars a year in the struggle region because of these cost savings we then took a look at whether transit access had improves for Ida

Developments on between the two halves of the period that we were studying and we found that it had improved in the earlier period only sixty percent of IDA developments had lockable access to public transportation and by the second Jack of the period that had risen risen to talk 74 person unfortunately we also

Took a deep dive using santi’s transit connectivity index to see what the quality of that transit service was and we found that the quality of the service has actually become declined between the see periods t spoke earlier about how important job proximity and access our keeping transportation costs low so we

Mapped items family developments against job centers and we’re struck by the fact that the two fastest growing employment corridors in the region and had very few affordable housing developments located nearby a final device that we use when we’re talking about housing and transportation affordability is case studies and so I’m presenting here case

Studies of three different items development that allow you to examine the underlying measures that contribute to higher transportation costs and as you can see moving from left to right the three developments move from an urban setting and inner ring suburbs and an ex urban location you can see that block size northern

Quadruples we strive in the progression from urban to exurban car ownership and DNT double and transit ridership for work commutes stress by seventy five percent you know once again I want to save it for our money it’s not the analysis rated since that’s important it’s what you get out of the analysis is

Important and as a result of this particular study the Illinois Housing Development Authority began requiring developers to use the Census Bureau’s on the map website and the local employment dynamic data that’s available there as a better measure of job availability in order to score points on their applications for housing financing Ida

Also decided to approve their qualified allocation plan which is the policy document it guides the scoring of the most competitive proposals for one year rather than two to give their staff time to study cmt’s other a recommendations so that they can be incorporated in a subsequent revision

Had faster rather than wait two years a third example takes us to Cincinnati where HDFC was used in conjunction with a number of other tools to highlight the community benefits that would accrue as a result of the proposed streetcar line the h analysis showed that the streetcar alignment at strong potential

For creating enclaves that could have lower combines housing and transportation costs using data from beyond the map website fancy map the region’s major employment centers and showed that the streetcar would connect with any jobs in cinnati downtown with the concentration of jobs in uptown we used our transit

Connectivity index to show that most of the reasons employment centers are well served by transit but that a few are really just beyond the reach and that as a region they need to be thinking about how to bring those employment areas into the transit fold our scan of the market

Opportunities for transit oriented development along the streetcar route showed that locations with the greatest short term potential that’s the math on the Left were located on the outskirts of downtown and that the longer term opportunities on the right were located in the over-the-rhine neighborhood a low-income minority community situated

Between downtown and left town this analysis helps Cincinnati secure 25 million dollar urban circulator grant for the streetcar from the Federal Transit Administration and 51 million dollars in ode act funding to build a streetcar when the new administration of Governor Kasich took over they would through all of the state support for the

Streetcar and the analysis convinced the Cincinnati City Council to proceed with a scaled-back version of the suite are using only local funds as opposed to scrapping the project entirely DC is one of the cities along with the bay area where several interrelated h 50 projects coalescing into an HT defined way of

Doing things felt way burden was a collaborative publication between the Urban Land Institute the Center for Housing Policy and CNT that presented the combined cost of housing and transportation in the metro area using 2006 data sets the report made quite a splash when it hits a DC media outlets

As part of our Uli CHP engagement CNC created a custom cost calculator for DC Boxton and bay area that allows you to personalized information about their household so to get a better fix under housing transportation cars once again as a result of this work the reasons Council of Governments recently

Set an H plus the affordability goal of 45-percent 459 regional activity centers as part of their region forward 2050 plan I can edition CNT is currently developing a scenario evaluation tool for the district that will enable their planning department to gauge the impact of different development scenarios on

Transportation behavior in Santa Fe New Mexico we’ve developed a training webinar for local housing counselors to teach them how to incorporate transportation costs into their counseling sessions we’ve also developed the packet of materials that can be given the first time home buyers to make sure they think about average

Transportation costs before buying the pilot was launched for spring and is currently being tested and evaluated by the Housing Trust counselors CNT hopes to take it to scale following feedback from their staff since we don’t have a custom calculator for every region in the country reverse the Santa Fe

Counselors to introduce their clients to the iboga website a bogo is a consumer-oriented derivative to love the index that allows users to compare average transportation costs by the addresses of the places they’re considering buying or renting and being the innovators that we are Santa’s research staff added a gas slider to the

Above website with friends as gas prices were rising so that visitors to the site can now also check how gas pricing increases will impact their costs and just so you don’t think that HD aged 50 index is all about big cities I not I’d be remiss if I didn’t highlight our work

In Grand Rapids Michigan where we help local stakeholders apply the h-60 index paid 28 largely rural counties we provided custom data to Grand Valley State University and their partner the grand rapids area coalition to end homeland homelessness which they use to write a report on housing and transportation affordability in there eight county

Region the Grand Rapids customization examined transportation costs for households of different sizes earning fifty percent of area median income if you go to our website the lowest level of income you can get to there is eighty percent and we provide only the typical household size for the reason and for

Extra dispatcher you can also take a look at local income levels on the web salesman this would CMT’s first-ever rural or analysis of housing and transportation costs it showed that in rural areas of the region households can spend roughly two hundred dollars a month more and transportation costs and

Similar income household to know the nice areas as I’m sure you’re all aware the federal sustainability sustainable communities partnership or cud do t and BPA has latched on for the concept of housing and transportation affordability and incorporated it as a criterion in the sustainable community using a planning grant program the community

Challenge grant program and the tiger planning grant programs and in addition each agency is established h puts the affordability goals as part of their strategic plans we’re not happy to rest on our laurels at CMC so in addition to all of the updates that keep referred to

We have several more applications on the horizon and if you if you as planners want to stay current on HD development and please sign up for our HD newsletter whose URL is at the bottom of slides you’ll receive a monthly update about new development website features descriptions of new projects new end

User resources information about new applications we’re very interested in knowing what you do with the age / see index so if you’ve already done something and want to send us a note on that via the chat box we’d be very happy to hear about it a bit more importantly one two three four

Months down the line after you’ve had time to let the salt print in if you want to get back to us with information about what you’ve done is the result of this presentation would be very happy to hear it I’m now going to turn it over to

Stephanie Mort who is going through to a demonstration of the a website Thank You Maria so now that Peter in murrieta have talked about the agency index its development and its applications we will take a look at the agency website we’ve actually decided to do this as a few screenshots with all of

The bandwidth and servers and computers involved with a webinar we figured this would be a more smooth way to go through this so on that note what you are looking at here is the HMT splash page you’ll arrive at this at HTA index dot org and as Maria showed this is the map

Of the 337 metro areas that are currently covered it is also actually a tool to help you select a region so what happens is if you hover over the maps and click on it you’ll do in and then you’ll be able to select a region from

Here if you don’t know the region or what it looks like on a mess like this you can actually hover over the regions and each name will pop up to help you select that late another option to select your region is if you scroll a little further down on the flash page

You’ll see a form box where you can enter in the metropolitan region name you can either use a drop down to see the list of all the regions covered or you can just start typing and an autofill feature will help you pluck the region that way so let’s go back up to

The mess and we’ll select this way so I’m going to zoom in Chicago because we live here and we’re familiar with it and you can see again if you hover over the region the name will pop up and then you’ll just click on the region and

Thing you’ll get two is that the website defaults to these side by side maps of our agency and index as Peter explained it’s one of the main focuses of the research is to expose the hidden cost of transportation adds them to housing costs and redefine the affordability so

On a left you’ll see the map of the traditional definition of affordability wear the yellow areas indicate average housing cost affordable to the regional median income households and on the right you’ll see them at where we’ve added transportation costs to the cost of housing and in this case the yellow

Areas represent places where HIV toss combined represent less than forty five percent of the area median income so while we keep talking about the area median income and fixing household characteristics I want to point out that up in the top of the math area under where the region is identified you can

See the definition of the typical household in the region so here in the Chicago area the regional media narrative and fifty two thousand dollars there’s an average of two point six people per household and an average of one point three commuters for household so those are the three household

Characteristics and will fit and run the model on so all of the resulting data that you’ll see on the website is solely a function of the built environment not these household characteristics as we’re holding those constants so now we’ll get into the maps a little bit i’m sure the

Navigation tools up here in the upper left look familiar to most of you they’re pretty standard for most mapping programs but you can use the plus and minus arrows to zoom in or japan around the map so if we can start to zoom in here you can see how that works and

You’ll get to a point where you’re zoomed in far enough where you can actually see the block group boundaries shown in green here those are what we constantly refer to as neighborhood and actually in the middle you can see the value for whatever variable you’re looking at for that block group if

You’re in a dense neighborhood with small block sizes or small block group sizes you might find these boundaries a little distracting and if that’s the case you can use this option at the top of the map to turn the block rebounds results and you’ll see if we click on

That you’ll see the same map just with the boundaries on and after looking at a map with the boundaries you can still see the block group values for whatever variable you’re looking at by clicking on the map wherever you click you’ll get four values like I did right there so those

۳۰٫۸ the top values that’s the average value for the blocker by quick driven the next value where it says Lagrange parts that’s the municipality or the place that I could have been the third value is the county and then the fourth value is the regional average value so

Again you’ll get that for any map you’re looking at or whatever variable you’re looking at so I want to get into some of the other features of the maps but first we will change the region just to get a dash it sounds a little bit so you’ll

See that up here where the region’s identified you can click on the box that says region and it’ll bring up again the entry box where you can type in the name of a region or the map where you can select a region we will zoom in to reno

Nevada this and again you see the default is this side by side of the agency index or its new view of affordability but we also have a few other preset comparison maps that you can look up so up here on the top you can see that there’s four tabs so right

Now we’re looking at the agency index so we can also click to look at gas cost info and what you see in these maps are the annual household gasoline expenses and the difference between the two maps is that we change the gas price so on the left-hand maps we’ve used the 2000

Gas price whereas on the right hand map we’re using the peak price from 2008 like to note that we keep all other variables constant here so we’re sending the same driving behavior between the two years and everything like that and all we’re changing is the gas price but

What that shows you is that areas that are more dependent on automobiles and have to drive more are much more acceptable to the fluctuations and gas prices their gas prices increase significantly more than the inner city areas where people can get by with driving less or have more options the

Next time you see here is the greenhouse gas index and this shows you the carbon dioxide emissions from sold audible use in two different ways so the map on the left shows a can per acre and the map on the right shows the emissions per household traditionally the map on the left is

What you might see and this kind of went to the arguments that cities believe more and bad but what we we showed this because we want to advocate for looking at emissions on a per household basis and when you see that when you look at this schema that you see that households

In the center city in a dense urban location fishing areas actually emit much less per household than the surrounding areas where people are forced to drive more and the last time on the test on the top seeding is a little bit different this one brings up custom comparison so what you’ll see

Here is a list of all of the variables that have anything to do with agency so we have the affordability and diffuse transportation costs various measures of gas prices and we have the greenhouse gas and packs all of egg scroll down right there all of the independent variables including the environmental

Variables and the household variables as well as housing costs variables so what this does is you can see the little buttons on over here on the side you can choose any two variables and set up your own side by side comparisons so here on the Left map we’ll look at oil for

Household and I’m equipped on residential density here to show that comparison zajety those are the two maps which is popped up I chose this comparison because it’s really kind of its to the heart of the agency index where you see hi residential density you very clearly see low auto ownership and

The inverse of that where we have low residential density you see high idle ownership and that really is one of the driving forces behind the agency index and as we’ve been mentioning all along all of the variables and measures involved in the agency index are calculated at the Census phosphorus

Levels so again you can zoom in and see the variation at that level and if you wanted to get into changing the variables and setting up different comparisons you can either go back up here to the custom comparisons or where you’re very variable heading is you can

Click on this little chains off change box either way you will get the full list of rivals again and you can set up any sort of comparisons you’re interested in looking at like to get out of this snow and show you some of the other textures

Of the site up here in the upper right of the math for easy display and change this little change button opens up a whole lot of cool features you can see right now what we’re just what we’re displaying is the legend and description so i just referred to this band ring

Here where you can see the basic legend of the map and the description of the variables if we click to change that you’ll get a whole list of different ways that you can display that i’m sorry this is a little blurry we were having some problems with the screenshots at

This point but i’ll walk you through so the second one is the summary table of statistics if you click on that what you’ll see is minimum values average values and maximum values for whatever variable you’re looking at you’ll see them both for the region as a whole but

Also for the viewable area of the map below so if you’re zoomed into an area smaller than the region the second column will just show you the statistics for what you’re looking at the next one you can look at is a graphical legends this shows you the count of block groups

Or neighborhoods in each of the colors displayed on the map below the next legend is a histogram I’ll note your the fuse get a little funkier as you go down them so I’m just going to kind of move through them quickly so any data geeks

Out there like us can go back and look at the menu on your own time but this shows a histogram of the distribution of data between the different bins you see here and the last legends view is a line graph so what this shows you is on the

X-axis you see the variable that you’re looking at so over here on the left you see autos per household and the y-axis shows you the three indices so you see housing cost is a percent of income transportation costs as a percent of income and H&T as a person is income and

What you can see there is how each variable corresponds with in the index that you’re looking at so in this case as auto store households goes up housing costs as a percent of income time to go up so you can set up some interesting comparisons that our history has a

Different input variables correspond with the different now the last three display options are a little bit different and that these are actually aggregation to us so you see population statistics household statistics and neighborhood statistics now for these what you do is you’ll select whatever you want to aggravate so

In this case I’m going to select population as an example and then you’ll get an option for focus so if you click on this you’ll see that you can focus in on the map area again what we’re looking at right here the whole region so that would be the metropolitan area a County

Municipalities or if you happen to be a New England you can see the New England towns and cities so in this case we’ll choose a municipality and then once you do that you’ll get a list of all of the municipalities in that region that you’re looking at so in this case let’s

Choose Reno and now what you see first of all we see is that the boundary of Reno has shown up on the mouth now this says that kind of teal color on the map and what this does for you is it aggravates the population statistics for

The City of Reno in each one of these bins as shown on them up so what you can see here is about 8,300 people living in block groups where the average Auto ownership is less than one point four bottles per household in the city of

Reno so again you can do that for the math area the whole region the county or any County any municipality in any New England towns or cities and you can do it for the population the count of how schools or the neighborhoods and in this case the neighborhood is defined as a

Census block groups so just to wrap up I would like to show some of the other supporting materials that we’ve included on the website you can see up here in the upper right of the little tabs that you’re probably familiar with those most websites but there’s a link to an FAQ

This is primarily focused on navigating the site and the various features of the site so a lot of what we just walked through there is also a glossary which defines just about every phrase that you’ll encounter on the website if you if there’s anything you’re not familiar with there’s open about sections this

Has most if not all of our agency reports the work that we’ve done with agency community profiles metro reporter and is just a little bit of history and non-agency there is a link to the method section so this has just a basic overview of the methods you’ll see at

The bottom the graphics that Peter showed as well as a link here in the middle to a much more detailed message paper if you’re interested in more detail there and just to wrap up there is a link to the mailing list Murray explains this but this is where you can

Do that so in the upper right if you click on mailing list so bring you to this entry form where you can give us your information was and subscribe to our agency selling this so that was a very quick run through of all the future herbs some of the features of our site

So don’t hesitate to submit your questions if you have any and we’ll try to go back and cover things more slowly if need be but with that I will turn it over to Stephanie I think the other Stephanie will facilitate the question isn’t yes um thanks so much all of 7am

Peed and Maria for getting us through an awful lot of information there it’s 105 now so we’re starting our Q&A a couple minutes later than we had hoped but though they really good job of keeping us pretty much on schedule on the questions that we’ve assembled here

They’re going to be a little bit out of the order in which they were submitted just to keep them uh you know sort of group together in a way so the first question we have is to please explain the distribution of hnt applications for example I think the person is

Referencing the map that we showed one of the earlier slides where shows a map of different applications and that was bidden to have to go into the other slides oh um where the applications that the agency index appeared to be concentrated in midwest was none in the

Southeast and and I would say and that the answer to that is that fancy has been very opportunistic in terms of where it has gone to try to apply the index where we’re interested in doing index applications in those regions that are interested in applying them and i

Would say that what this map shows our locations that have prepped a strong interest in the H+ T affordability index I would love nothing more than to go to the Southeast United States when we get next year’s Groundhog Day blizzard in Chicago and I so I encourage you if

You’re in the South Beach particularly Florida along the coastline give me a call and i would say a second second determinant of where applications have where we r CNT is engaged is that we’ve been very interested in trying to get a range of applications so yes we could do

Regional applications all over the country but it wouldn’t accomplish much in terms of reaching a broader audience and trying to show different ways to use the index so i would say we’ve been up for mystic when it comes to geographic distribution and very deliberate about making sure that we have transportation

Applications housing applications regional planning applications even consumer-oriented applications and because we want to reach out and touch as many different audiences as possible okay great thanks for Rhea we have a the next question um I think I know who’s going to answer this but i’ll go ahead

And sort out the panel again so someone said glad to hear you’re establishing a broader national transit database how are you separating New Jersey from New York City metropolitan area since New Jersey has a statewide transit system / provider and data that differs from New York City well well maybe how long

Starting time in but will do so love it um so yeah that’s a very good point this is part of the reason why we’re doing a more detailed and deeper dive into you translate accessibility what we so let me just talk a little bit of a sort of

Our plan and what we’re working on right now is we’re actually looking at how transit agencies provide their data to websites like Google and others that do transit the transit planning you probably have heard of depending on your level of transit data familiarity is something called GTFS which is a

Standard way of transit agencies to supply their both route or stop locations for post route frequency and service so we’re plugging into that infrastructure and we’re developing and getting these data from from as many transit agencies as we can and compiling them so the New Jersey New York Mets

Pulmonaria really poses an interesting problem one of the things maybe I’ll just respect the previous map his look at that northeast corridor where all those metropolitan regions are surrounding each other and in fact new york city metropolitan area in the way that we used to fund the definition that

We’ve used is actually fairly limited to the five boroughs and Westchester County New York you notice that long island is its own and and then Newark and Jersey City and and places on on to the west are all on separate metropolitan area so so yeah it’s important that we did can

Attribute iskcon and attributes the amount of spending on on transit to them through the correct geographical area so that’s that’s something that we’re going to get more collected within this next couple next upcoming iteration and I would just like to add to what you said here yeah while New Jersey has a

Statewide system there are some metropolitan our city or smaller transit agencies that supply sort of niche markets I think within the state that’s right there I could go on right out but suffice it to say that yeah this did need a lot more details working and this

Is a good example of why we need to do so I hope I answered your question do you have anything to add to that Rick well our next question then is how have state and local taxes particularly property tax has been considered in the index okay so as Peter to subscribe we

Use the selected monthly on their costs from the Census to capture ownership costs for housing and I’m sure somebody will correct me if I’m wrong but I’m relatively confident that this lesson of the ownership class data then you get from the census includes property taxes so we have included it to the extent

That it’s included in that census variable I might just chime in a little bit on and whoops rent is a little bit of a conundrum what renters how those taxes trickle down to renters it’s a little bit of a little more complicated but we think that I mean if you believe

In sort of rational markets and with it and supply and demand on that the gross rent should should should encompass back those costs as well okay thank you guys the next question is what methodology was used to determine employment centers in the Chicago example okay well I can

Take that a lot of work on developing that so we use as Maria said the longitudinal employment data from the Census and Department of Labor up and before that actually for the our model although you guys use that for the Cincinnati work but to develop our model

In our employment access we also use something called the sea TPP which I’m carrier familiar with is the census transportation planning package which relates census information to employment similarly that the LED eight it does so in those data you get the number of jobs the actual where employment happens not

Just where where where workers live but also where they work by census tract in the sea PPP case and it actually at the block level group level for LED and then we what we do is we have basically a GIS algorithm that I’ll just walk through

For please we can talk more offline if you want it is to look at so we look at a region we pull out the census tract with the highest employment in it we call that the center of the first cluster and then we look at clusters that are intersect

That that are tracks that intersect that clusters that are above a certain threshold so so many workers per acre it within those areas and then we just keep doing that until we run out of more tracks that have are above that threshold and then we constitute as a as

A employment center that we do it again we find the next highest employment census tract with employment that’s not in a cluster yet and keep iterating on that until we’ve we found all of the places and then we only call it a let me just finish that message we find all the

Places that have clusters of census tracks with employment above above given threshold um our threshold fairly low we use seven workers per acre although i think we fussed with that down i’m not sure what we used a word for that i think that what we did yeah um and so

Then in the end we look at those clusters and we demand that there’s a certain thresholds of number of total numbers of workers within their to call it a regional employment center and I think that was a 5,000 or 10,000 depending on nom on the region and and

What in what works so so I hope that that maybe is a little long we live in here we have several questions just whatever in this one is 114 they actually have a very easy one Pete I’ve been soon well a couple of you today to

This one what is the data source for average block size oh we use the census definition of a black which is is not they found on demographics it’s actually based on this the tiger line file which is based on streets and boundaries and water so it’s just simply connected

Benefit our lines to form a block so it’s a physical definition and this night question I am is oh i thought those edition one on how do mortgage lenders respond to this index has this approach that accepted anywhere and i assume the questioner is asking has the approach of agency of her

Ability kelly also brings in the concept of location efficient mortgages the question is how do mortgage lenders respond to the use of Asian standard a little less than a decade ago CNT i convinced fannie and Ginnie Mae to approve a loan product that was called a location efficient mortgage that would

Allow a higher income to debt ratio by virtue of purchasing a home in a location efficient place that would allow the buyer to get by with fewer cars and lower transportation costs unfortunately that pilot program and it was a pilot that was implemented in only a few places across the country was not

Very well used because we were in that period of the frantic run-up in home purchasing that led to the collapse in two thousand six and two thousand seven and lenders and brokers were so free and easy about as the debt to income ratio that people didn’t even need the limp as

A product to qualify for a mortgage they were qualifying for mortgages well beyond their means and as a result the product was never really used C&T has places high priority on trying to resurrect the LEM in this day and age of greater scrutiny and greater care related to residential lending we’ve had

Several drafts bills on the hill refer to location efficient mortgages and include recommendations for resurrecting them we have yet to succeed on that front but we will keep at it and we’ll keep you posted through a newsletter okay Thank You Maria there’s a question here that have to do with the 45%

Affordability standard the team’s recommends the question is you think that spending 30 first sentence of income on housing and fifteen percent on transportation is still one affordable or do you think that’s actually jealous 5 i’ll start well i mean given the statistics and what i showed with them

To the bureau of labor statistics Consumer Expenditure survey it look it looks like it sort of the realm of feasibility is that we would get a good population of people and and it would allow for better decision making in terms of where you where you choose to

Owner or rent a house I would say that you know portability indices in general are somewhat arbitrary in nature on where did the thirty percent come from our founder likes to say it’s related to some old english poem about a week’s wage in a and a month house or some for

Brinster I don’t know I’m not illiterate enough to quote that but yeah and and so so what we were trying to do here when we says is somewhat be aspirational in scope i talked about you know places like Baltimore and Chicago being and the thirteen to fifteen percent sort of

Spent on transportation and if thirty percent is it’s a given sort of standard then it kind of makes sense that 45 should be bored should work I like to say you know you shouldn’t just shoot for the average where it’s 18 because you know um maybe we should say that all

Americans should weigh the average weight but we know that that’s not right these are hurt we’re all a little to blow maybe have sat or set on our touch too long I need to get out and chase the bus more I’m degress I so so so it’s

Aspirational in nature but but but as you can see with the numbers in the way we’ve shown this that it’s there out this I think will set a standard that will allow for the smarter and better choices on Moran except they have any we want to say I matter yeah I would just

Say as the place that Pete cited all happened to have fixed guideway transit system and I want to you know emphasize that achieving 45% affordability on housing and transportation costs if it’s something that happens in smaller places in places without fixed guideway service it is largely a function of the density the

Block size the mix of uses and the access to jobs that really drives that equation and so I don’t want to leave you with the impression that you have to have a fixed guideway transit to achieve that level of affordability so thanks we’re just getting our flood our flights

To show the last one there was the contact information if we were alerted that that would be a good idea to show that now so moving along here we have the next question is a poor or a belief as well don’t sweat backers were analyzing this you of the opportunities

Now and for the short-term measures we took a look at market statistics we looked at a number of jobs we also looked at retail figures for the area to get a sense of what was happening and we looked at daytime and evening populations the long term scan removed the market characteristics and

Substituted in the H+ T index characteristics to demonstrate places where the block size the street grid pattern the opportunity existed without major configuration for achieving low combined housing and transportation costs so I would say that that in a nutshell I could give you a longer description but I think for the

Moment settle third I think your mouth is a quick and easy one you mentioned that you’re going to be expanding your coverage in the 2011 release do you have a list of areas you are planning to cover in this release we do um we don’t

Have it up on a website we are cut because we’re using the 2005 to 2009 american community survey data it’s indicated within those data that you should you be using the metropolitan and tear even from the OMB definition for 2,000 feet so what that means is if you

Look at the LMD bullets in 2002 8 core based statistical areas those will be the air here is recovering oh did you want to contact one of us we could send you can we should put it in the next year’s letter something yeah it’s kind

Of a long time I was 940 much about Mario’s yeah wouldn’t you know so it’s really big one with a linksys yeah that’s good idea so I similarly when will the agency annex be updated with the latest census tract oh boy so there’s a lot of issues with census

Tracts but when we do when we do update the young data with the ACS rolling average from two thousand five to two thousand nine five year average that is using what’s called the 2009 geographies are really based upon the 2,000 geographies with some changes and there has been errors in those in those

Definitions at the Census Bureau which they know about and have marked on their site so the challenge to get it all right um so if you’re talking about the new census tracts meeting the 2010 these are the ether the collection while we are using the twenty ten blocks to

Aggregate up to block groups and just tracks 444 data that does not that we don’t require demographics in other words of the socio-economic measures which is so the new census but rather try say is the new census is different than the old census because it’s only counting people and it breaks across

Race gender age things like that that require that are required by the by the Constitution to develop the house of representatives on districts they are no longer doing with the Destiny incentives what we used to call the long form where they do the social economics household

Income autos / house all the data that we need so that’s why we’re having to rely on the American Community Survey which will go on forever at this five-year rolling average so in some sense is a feature but it’s a more highly sample database which was going

To be a struggle and we’re not sure when the transition will glow from the 2,000 based census geographies to the 2 2010 based census geography there was a lot of information maybe you didn’t know but it sounds like there was a pretty focused questions okay thank you so

Here’s another question here’s what the gas cost index I think in the gas price factor applied one in one single gas price of our whole region and as such it shows a lower impact in dense urban centers however gas price varies greatly over a region for example here in

Chicagoland where we are so we know this well river next paid three dollars and eighty cents a gallon while urban core dwellers pay four dollars fifty cents a gallon is there any way over more detail having a more detail analysis with specific gas costs for neighborhood um

So so I totally recognize you said is correct the problem is access to data that gas price data that comes out of the National Energy Information Service out of Oak Ridge Tennessee from the Department of Energy is really regional with a few metropolitan areas included

So so if you want to know what the gas price in and pick a city in Des Moines Iowa you’re really using the Midwest average so it’s even worse than you think from what to your question but but but but those are the data that we have

Accessible that’s why we put the slider on a bogo so that you could actually tune in your own gas prices and say what what those look like so from households perspective you could say well if I am going to fill up downtown yeah I’m going to pay 36 hour for

A gallon but if i seem to drive and waste the gas this is always a little catalyst I never quite understand I could drive the Indiana and even say last because there and which I can’t believe that then we would actually do that um but so would vary but so from an

Individual point of view the gas lighter i think is our is Erica I’m ticket to that I’m a regional planning kind of point of view where you’re trying to look at where these advantageous and not to develop housing versus gas prices I think this is a conundrum that belongs

In the category of lack of good data so as we get more detailed data I similarly we have two other questions I had to do with well does it count for this and what about this and I think that they’re similar entry because they also dual data availability members have said

Helpful greenhouse gas impacts data it is great but what about estimates for daytime population where businesses in the downtown suburbs etc any plans for this kind of data in the future and the next question of similar is a carbon footprint by household is interesting was free to factor into these

Calculations I think the answer to both of those is the same and that the carbon dioxide emissions that we’re looking at are solely the function of household auto use so we’re not taking into account businesses we’re not taking into account transit is just how much people drive that’s the only factors that we’re

Taking you down for that right so it’s not meant to be a complete carbon footprint but maybe just a little tweak on that first first question just do we look at the total daytime population of a region and I what we found in other and not in this work but in a similar

Study that we’ve done about where buildings could have location efficiency office buildings and such we’ve seen that that the combined workers and and residential variable actually does drive some some some mode choice decisions but that’s more about people coming to a location to work so so there that’s an

Interest question and one that we keep keep coming back to when we do our research so we’ve got about one more minute left to get a question in here and I think probably come to choose to a question here and so if you could keep the thing but the next

So that I want to cut off the next one after that he’ll be on the next question is there are many variables I think the person means that that involved in location selection of picture saying and some may not be easily quantifiable how does your model take them began in to

Account for example availability of quality education university crime and impact those factors on this whole equation of affordability majority answer is we don’t those are those are intangible as you said and and we’re not advocating that anybody do anything what we’re saying is we’re trying to give complete information so you make an

Intelligent choice so if you’re going to move to a remote suburb because the tools are better and crime is for seems to be less that that’s your decision and but know that you’re going to spend on transportation and take that into consideration and you’re going to close yourself to the volatility of petroleum

Market as I always want to put in there okay great and I am afraid that I’m we’re going to have to close the rest of the questions at this point moving in hand it back over to Brittany to close things out foreign so thank you to everyone for submitting questions we did

Our best to get to all of them but if we did not get to your question please a signature email we’d be happy to get back to you you can do that either via this questioning questions comment tool here in the webinar or you can use the survey instrument that Brittany will be

Posting to you later to submit your question that way and also your email address and email addresses are up there on the street so thanks a lot okay thank you stephanie i also want to just think say thanks to a Peter Stephanie and Maria for a great presentation and also

Thanks to Stephanie scholer and Matt work for a for helping out with organizing this webcast and making it possible so for those of you who are still in attendance I just want to go through a few reminders about logging your CM credits and about our recording of this presentation

Alright so just a second here well I get load up our slides alright so first off to log your CM credits for attending today’s webcast please go to ww planning org slash cm select today’s date jun 24th and then select today’s webcast introduction to the asian t affordability index and applications and

Planning this webcast is available for one and a half cm credits we are also recording today’s session so you will be able to find a recording of this webcast along with a six slide per page PDF at wwu ta PA org slash webcast archive so this concludes today’s session and I

Want to thank everyone again for attending

ID: FaSzJkGwLJQ
Time: 1344116527
Date: 2012-08-05 02:12:07
Duration: 01:31:13

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