This semester I took an econometrics class because I got an MA with the bare minimum of quantitative classes. For the class, I wrote a paper asking the question, “Are consumers willing to pay a premium to live in dense urban areas?” It’s easy to see that urban density is correlated with higher housing prices, but this could come from many factors such as people having to live in dense cities to find jobs or to earn higher salaries or from supply restrictions that impact dense cities more than suburbs.
As a proxy for cities’ urban qualities, I used Walk Score. Walk Score is based on residential distance to amenities, block length, and road connectivity and ranks cities on a scales of 100. It is designed to test the feasibility of living in a city without a car, but it excludes some factors that are often considered relevant to facilitating pedestrianism, including street width, sidewalk width, and population density. Still, I think Walk Score provides a pretty good measure of a city’s urbanist quality. The correlation between Walk Score and median house price is pretty striking:
To test demand for urban living, I wanted to control for the economic factors that drive demand to live in a given city. I tested the impact of Walk Score on median house prices controlling for household income, unemployment, and cost of living. The sample includes 259 cities for which I had Walk Score data and house price data from Kiplinger. The results suggest that for a one-point increase in Walk Score, we can expect a .5% increase in a cities’ median house price, and this result is statistically significant.
In another way of measuring the same question (an IV regression using the year the city was founded as the instrument), I found that a one-point increase in Walk Score can be expected to increase home prices by 3%. This result is also statistically significant, but I have less faith in this model.
For the most part, the other studies that I’ve seen of Walk Score’s relationship to house prices look at one city or a few cities and control for variables like a neighborhood’s crime rate and housing quality. While there are obvious advantages to these more detailed, local studies, I think the national view gets around the sample selection problems that make other results ungeneralizable.
I’d be happy to hear your criticisms of this model — what important variable are omitted, etc. I think there is a lot of room to study people’s preferences for urban form. As Stephen has said previously, looking at where people live without controlling for other factors gives us a better sense of allowable land use than free market revealed preferences, but looking at home prices while controlling for important variables can remove some of this bias.
Thanks to Eli Dourado for helping me think through this model, but of course its problems are my fault.
[Note: I had originally said that the house price data came from the Census. I realized that Kiplinger does not get this data from the Census as their Statistical Abstract only covers select MSAs. The data was collected by Clear Capital, but I haven’t seen it publicly available from them.]
Jason Stokes saysMay 18, 2012 at 12:07 pm
Great thoughts on this topic, and great work. A few questions:
What’s the statistical results of your model? Can you post the data behind it?How about controlling for population density?
What about walk score by census tract or ZIP code? I think that may paint a clearer picture than MSA data – as MSAs sometimes contain both walkable and non-walkable parts of the same down?
Kenny Easwaran saysMay 18, 2012 at 1:41 pm
There’s some value to having this nationwide as you do, but there’s also important value in looking at prices within metro areas. The work of Andrew Gelman has shown that although the median income of a state strongly correlates with Democratic percentage of vote, income of individuals within each state is strongly correlated with voting Republican. (The explanation is that poor people everywhere vote equally Democratic, but rich people in poor states vote especially Republican, while rich people in rich states only vote slightly Republican.) It would be interesting to see to what extent your data are revealing that individual houses in walkable locations have a higher price, and to what extent people want to live in a city that has lots of other walkable houses.
Anecdotally, it definitely seems to me that some of the richest neighborhoods in most cities are highly un-walkable. Of course, that last point may also go away after controlling for the size of the house. It may be that 4 bedroom homes are generally unwalkable, but that the few walkable ones are even more expensive.
Bill Ruhsam saysMay 18, 2012 at 2:36 pm
I will second Jason Stokes’ comment: The declaration of significance needs to be backed by specific data. Significant to what confidence? How significant? This is this the sort of thing I’d love to hit people with, but without more info I can only say “interesting.”
Emily Washington saysMay 19, 2012 at 12:28 pm
Thanks for the comments! I have included the results in a new post. The MSA data is definitely not the ideal way to measure the other data, but I’m not aware of unemployment, for example, being available at the more local level. I hope to do more with this though, so I will look into it further.
Eric saysMay 20, 2012 at 4:00 am
What do you mean by a “house”? Do you mean all residential units that are for sale? I’d guess that on average residential units are smaller in walkable than non-walkable areas. Which should make the former type cheaper, but we see in fact that it is more expensive, further highlighting the apparent role of walkability. But if you only mean detached houses, there are fewer of those in walkable areas, so the correlation is less meaningful.
Emily Washington saysMay 20, 2012 at 11:18 am
Great point. The data is for houses, not condos, which are more much more significant in the most walkable cities than others. A better independent variable would be some type of housing cost index that includes condos and rental units, but I’m not aware of one.
awp saysMay 21, 2012 at 7:43 pm
median price per square foot of residential space.
awp saysMay 21, 2012 at 7:46 pm
awp saysMay 21, 2012 at 7:49 pm
There has been some research on this( google scholar search for “walkscore and home prices” posted above), but I really think that walkscores are just proxying for density, and places are dense because they are valuable(demand for living there is high) which explains the higher housing prices.
Emily Washington saysMay 21, 2012 at 8:47 pm
That would be much better. Do you know if that’s available? I haven’t been able to find it, at least not without paying.
awp saysMay 21, 2012 at 9:39 pm
yea, proprietary data stinks.
Seems like there might be a lot of problems with this, but maybe if you have median sqft
median price/median sqft=price per sqft of the median house in a MSA?????????????
Emily Washington saysMay 22, 2012 at 9:28 am
Interesting idea. It might be a problem that the distributions of prices and sizes are different from each other and different across cities, but it could at least give an idea of the price/sqft. I will try to look into finding data on median house size.
awp saysMay 23, 2012 at 5:09 pm
It IS a problem that the distributions are different within cities. One might be able to sell it as
median price/median sqft~ proxy for median price per sqft ,don’t make strong claims.
I just remember back in the 00’s that what shocked me about L.A.’s median price reaching 500,000 was how little house that 500,000 bought you.
Eric saysJune 11, 2012 at 9:43 am
Price per square foot is in there…