Ep. 117: Road Scholars on Density, Displacement, and Driving with Dan Chatman

Does building housing near rail stations reduce driving, even if it prices out lower-income residents? Dan Chatman's research suggests the answer hinges not on who lives there, but on how much housing gets built. 

• Chatman, D. G., Xu, R., Park, J., & Spevack, A. (2019). Does Transit-Oriented Gentrification Increase Driving? Journal of Planning Education and Research, 39(4), 482-495. https://journals.sagepub.com/doi/abs/10.1177/0739456X19872255
• Chatman, Dan (2015) Does Transit-Oriented Development Need the Transit? Access Magazine. https://accessmagazine.org/fall-2015/does-transit-oriented-development-need-the-transit/
• Chatman, D. G., Rodynansky, S., Boarnet, M., Comandon, A., Snyder, B., Patel, K., & Atkins, J. (2025). Assessing the Quantification Methodology for the Affordable Housing and Sustainable Communities Program. https://escholarship.org/uc/item/99j4s0bp

Transcript

Madeline Brozen 00:00:05
Hello, this is the Housing Voice podcast and I'm Madeline Brozen. I'm today's host on another episode of Road Scholars, a Housing Voice transportation detour.

In today's episode we are talking with Dan Chatman about what displacement means for how much people drive in transit-oriented areas. His work examined TODs and the net effect on driving when lower-income households are replaced by higher-income residents. The big takeaway is that upzoning reduces displacement, and if that upzoning occurs near rail stations, it can also reduce regional vehicle miles traveled. We're going to get into the details of those findings and much more, like the most succinct definition of gentrification and displacement that I've heard, and it's hopefully useful to you as well. 

Today's episode is brought to you by Transfers Magazine, a research publication of Pacific Southwest Region University Transportation Center. You can find more approachable transportation research content on their website, transfersmagazine.org. 

As always, the Housing Voice podcast is a production of the UCLA Lewis Center for Regional Policy Studies, with production support from Claudia Bustamante, Brett Berndt, Shane Phillips, and Tiffany Lieu. Please send any of your feedback for Road Scholars to me. 

And with that, let's get to our conversation with UC Berkeley's Dan Chatman. 

Dan Chatman is the chair and professor of city and regional planning at the UC Berkeley College of Environmental Design, where he studies how transportation systems and land use shape travel behavior, commonly focusing on policies intended to reduce driving. Welcome to Road Scholars, Dan.

Dan Chatman 00:01:54
Hi, thanks for having me.

Madeline Brozen 00:01:56
And I'm joined by my co-host and Road Scholars passenger, Juan Matute. Hey, Juan.

Juan Matute 00:02:01
Hey, Maddie.

Madeline Brozen 00:02:02
Dan, we start every episode the same way by asking our guests to share a memorable transportation experience. It can be funny, frustrating, formative. Your call. What's a journey you want to recount with our listeners?

Dan Chatman 00:02:15
Gosh, interesting question, since I wasn't prepared for that prompt. But the first thing that comes to my mind is, I was a Peace Corps volunteer in Botswana many years ago, an outpost called Ghanzi in the Kalahari Desert. And the thing about Botswana at that time was that very few of the roads were paved and there was very little in the way of transportation between towns. But there was also very little population in the towns, and so there was lots of people traveling around at holidays. They were traveling between work and their ancestral lands, they were traveling to the capital city. And so I spent a lot of time hitchhiking, because that was the way that you got around in those days. You'd wait for a truck to come by and you would try to flag them down and haggle with them over the cost of the ride and get in the back and then endure a harrowing four to 12 hours of very rough conditions and all sorts of elements to get to your eventual location. So that was pretty formative in some sense. I'm not sure how it relates in any way to my research, but that was how people got around those days.

Madeline Brozen 00:03:15
I mean we just want to paint a picture of something interesting, you know, outside of research, so thanks for that. So the article we're going to discuss today was published in the Journal of Planning Education and Research and it's titled Does Transit-Oriented Gentrification Increase Driving, along with his co-authors Ruoying Xu, Janice Park, and Anne Spevack. They look at the neighborhood, regional and state changes in how much people drive in transit-oriented development areas when new higher-income residents enter and lower-income residents leave these areas. In planning circles, transit-oriented development is a key strategy of coordinating new housing development to change how people travel with less driving and more walking and transit use. But one of the critiques of these developments can be that these new higher-income residents may mean a decline of lower-income households who rely on transit the most, and these are kind of described as displacement of residents incidents. So in these cases, is TOD or transit-oriented development still good when you have these two competing forces? So we'll get into how they conducted their analysis. But to set things up at a very high level, we're going to be talking about areas near rail stations in the four major urban areas of California at two points in time to see how household income and proximity to rail stations interact in shaping vehicle miles traveled.

Juan Matute 00:04:36
Dan, before we get into findings, I wanted to ask about the setup. What research question were you trying to address with this paper, and why?

Dan Chatman 00:04:46
I mean we were trying to look at, there are two really different research questions that the paper addresses. And one of them it addresses really highly quantitatively, looking at data from household travel diaries, and the second one it was looking at it in a bit more of a crude fashion, just trying to see what was happening in neighborhoods near rail stations. And so I'll address each of those in turn. So the first question was, well, if you compare people who live near rail stations in these metropolitan areas in California, how much do they drive and how does that vary with their income? So one big question in understanding what the likely net impact is on total vehicle miles traveled in a region is the question of the relative levels of vehicle miles traveled of high-income households near and farther away from rail and lower-income households near and farther away from rail. And the previous assumptions about this question had focused on the fact that if you look at the people who live near rail, who are of higher income or lower income, obviously it's not going to come as a surprise to your listeners to hear that people of higher income drive more. Even if they live near rail, they still drive quite a bit. If they live farther away from rail, they drive less. The question is, how much less? And is that differential greater or less than the differential when you look at the low income level of driving near and farther away from rail? And so that was a basic question that we were trying to answer, and it turned out that, in fact, the reduction of driving associated with rail proximity is higher for people of higher income, and the reason for that is partly because they start off at a much higher level of VMT when they're farther away from rail, and so an equivalent percentage reduction, in other words, for a higher income person is going to be a net reduction in vehicle miles traveled. So that was one of the big questions that we were trying to just look at descriptively, and then we tried to do a controlled analysis. The second question that I alluded to is the question of, well, what does happen in terms of neighborhood change near rail stations. And in this period of time and, I think, in many other locations in the United States, this is still true to the present day, densification happening near rail stations isn't typically associated with large reductions in low-income households. It's more typically associated with large increases in higher-income households, and so if you don't have a displacement happening in large numbers, then what this net effect means is that there's a reduction of VMT region-wide compared to a counterfactual in which those higher-income households did not locate near rail.

Juan Matute 00:07:07
And so was there previous research at the time that you were interested in critiquing or adding on to?

Dan Chatman 00:07:15
The previous research that you know we cited in the paper and sort of engaged with in the paper, hadn't really done the explicit work to try to talk about what would have happened in the absence of a transit-oriented development policy. It's easy to just say, oh well, what's happening locally? And, as I alluded to before, what is happening locally is, yeah, absolutely, if higher income households are coming in, the per capita VMT is going up. Not only that, if you look at a bunch of people coming in to an infill, an urban area, that's kind of an infill area near transit, and there's a growth in the population, what's going to happen on the roads? Absolutely, the driving is going to go up. And people were focusing on that and they weren't thinking about the question of, well, but wait a second, what if, instead, those households had not moved to the infill areas? Where would they have gone? And if they had gone to suburban areas, then the regional effect on VMT would have been an increase in VMT in comparison to an intensification policy.

Juan Matute 00:08:11
It's that consequential analysis that you're adding here.

Dan Chatman 00:08:14
Or you could say, there's two different ways that I think about it. One of them is it's explicitly regional, or even global, and the second way to think about it is it has to set up an explicit counterfactual. What would have happened in the absence of this pattern? So if there wasn't a bunch of people living locally, then what would have happened? And you have to answer that question explicitly. And it's speculative, but it's only when you answer that question explicitly that you can actually get at, okay, what is the VMT effect?

Madeline Brozen 00:08:45
So, picking up on VMT, let's talk about what you actually measured. So your models are predicting average daily household vehicle miles traveled as a function of the neighborhoods that people live in, the population density, the residential density and the household characteristics, or who they are, how many people they live with, whether they have children, if they're employed or retired. But first I want to talk a bit about just measuring driving, because we have a lot of policy in California and across the country that is trying to reduce vehicle miles traveled. But even as a transportation researcher I don't think I could tell anyone, and I really had to like look it up, of how do we even estimate how much we drive? So before we get into what you did in the paper, can you talk a little bit about how, you know, whether it's county or state actually estimates vehicle miles traveled?

Dan Chatman 00:09:35
Gosh. Yeah, that's an interesting question and I'm going to be working, I am in the process of starting a project with Mike Manville at UCLA and Susan Handy at UC Davis and Marta Gonzalez here at Berkeley, looking at the state's efforts to measure VMT and to try to estimate the impacts of policies that are intended to reduce VMT, and so it's quite relevant and something I think about recently quite a bit. So there are different ways that VMT estimates are. You know there's all kinds of VMT estimates out there. A lot of them actually come from models. They come from models that are used for the purpose of planning, that themselves are not necessarily very accurate, and they're models that are meant to kind of represent, well, what's going to happen if you develop a housing project here, how many miles are going to be driven by the residents of that housing project. And, as those of us in transportation are long very familiar with Institute of Transportation Engineers, per household VMT estimates or trip estimates. These sorts of things are often the basis for the models that are used to come up with VMT estimates. And in recent years there has been more of a reliance on products provided by companies like Streetlight and Replica, which are based on phone data and some imputation, using methods that are not completely transparent to take phone movements and translate those to vehicle miles traveled. So those are two important sources of estimates. Then there is also highway performance monitoring system based estimates, where you have traffic counts and you actually have counts of vehicles on freeways, and so you have freeway miles traveled estimates. And what we used in comparison to all those other estimates of VMT was data from a travel diary, and travel diaries can vary. This particular travel diary, the National Household Travel Survey, and this California Household Travel Survey, are both essentially using a self-reported information provided by people who are filling out a 24-hour account of everywhere they went and everything they did during a day. And actually we could get into the weeds here, because we have two different data sources, one of which uses an odometer estimate, the other one which uses the travel diary, and so it's a good question, because the answer is, there's about 18 different ways of doing this. I do think that there is a fair amount of concordance between them, so you know you're going to get relatively similar estimates of a per mile average, for example, using these different sources. That's a long answer. That's a lot of words.

Madeline Brozen 00:11:59
Hey, it makes me feel better, because I was like, is this really that hard to like Google and figure out? It didn't seem that straightforward, so I think it's reassuring, as a transportation researcher, that it's you know. You have to actually get in the weeds to understand that.

Dan Chatman 00:12:13
A little bit.

Juan Matute 00:12:14
So how did you control for residential self-selection? So the idea that people who already hate driving or can't drive or don't want to drive are the ones who choose to live near transit rather than the built environment itself changing their behavior?

Dan Chatman 00:12:29
I've done a lot of work on that topic, residential self-selection, and the short answer is we didn't. The longer answer is to talk about the expected effects of controlling for residential self-selection. So the idea of residential self-selection is that you know, if you look at cross-sectional differences among households and their levels of vehicle miles traveled, that you can't attribute those differences to the differences in the built environment, because, in fact, the people who go and live in places that, let's say, make it possible to walk more or drive shorter distances, that those people who choose to live there are themselves different. I think the way that I think about this, and this is something that's a completely different conversation about the residential self-selection literature, is that there's good reason to believe that, if anything, we're underestimating the differences in VMT associated with built environment characteristics when we use conventional methods of controlling for self-selection, because it's not one or the other thing, it's actually both. That is to say, when you say that people's preferences are what caused them to drive less, that's only partly true. It's also true that they need to have an environment that enables that preference to be expressed. So to say that the environment by itself doesn't cause something is a misleading. It's actually sort of an analytically impoverished idea of what we're talking about. When we talk about self-selection, what we're really talking about is the combination of opportunity and motivation together, and the built environment is the opportunity and the motivation is the self-selection. So, long story short, not controlling for this, in my view, is not a fatal flaw in estimating the relative differences between higher and lower income households. That said, you just brought up another example of how this field of research is complex, because, in fact, that we would like an instance where we could randomly assign people to different built environments of high income and low income and then see how much they drive, and then we can make maybe stronger claims about policy interventions. But if what we're trying to do is just see what happened though, which is kind of what this paper is focused on, then we're kind of okay, because what we want to see is, well, what happened, how much are people on average driving in these different environments? And that information is helpful in understanding whether or not we think that TOD is causing VMT to go up or down. And that was really where I was coming from, or we were coming from, in this paper. We understand that there may be issues here with people being displaced by transit-oriented development policies, particularly those that are not about providing greater housing opportunity near transit. But in those cases where greater housing opportunities are provided near transit, the question is, are we going to reduce VMT or not? And I think that this study, and other studies like it, by the way, that have done similar things, have found, is that yeah, we are going to reduce VMT. That way, it is pretty clear.

Juan Matute 00:15:20
So moving on to the regression results, the controlled regression analysis is where things get a bit more nuanced. In the NHTS models, the interaction between income and rail access is significant in the pooled California data, but not when you restrict to just the Bay Area or LA. Then the CHTS, the more spatially precise data set, shows a consistently significant negative interaction. Higher income means larger VMT reductions near rail. How do you square those two data sets?

Dan Chatman 00:15:52
Well, the way I view that... I mean, first of all, the data sets are different data and they are measured with different precision, and you've already pointed out that the CHTS data are more and more precise, and so if you had to choose one of the two data sets, you'd probably choose the CHTS, the California Household Travel Survey, because it's probably giving you a better understanding of rail proximity, specifically because that's the way in which the precision matters is actually better measuring proximity to rail. But the other thing I would say is what's consistent about the way I try to say this in the paper was here's the following thing that's consistent: whatever's going on, there isn't greater VMT reductions for poorer people. What's going on is either there isn't much difference, so that we can't talk about gentrification as being a problematic thing in terms of VMT, or what's going on may be that if all you care about was VMT, you might prefer gentrification to happen, and that's not a popular thing to say in planning circles and that's why it's somewhat controversial and that's why, when this work was funded by the state, there was a lot of pushback and a lot of concern about it. But the fact is, the numbers are what the numbers are, so there's not much to be done about it. But the other thing I would emphasize here is, I mentioned the second part of the analysis, which is the question of what is happening in the neighborhoods. Are people being displaced in census tracts near rail over this period of time? And the answer was well no. We couldn't find any examples in the data set that we were working with for the census tracts between 2007 and 2013 that were near rail in California, where there was a significant kind of reduction of the lower income population. It just didn't happen. Instead, there was maybe a very small reduction, but mostly there was a state about the same where it increased and then the higher income population grew. And under those circumstances, all we're talking about is, is there any differential among any of the groups, between places that were farther away from rail and near rail? And if there is, then a densification scheme near rail is going to reduce VMT and it could reduce it by quite a bit. According to this analysis. And I must say, by the way, that I and other research have argued and shown with data that it's probably not even about rail proximity. It's probably more about other built environment characteristics that are correlated with rail, one of the most important of which is parking availability, but we have poor data about parking, and so a lot of what we may be picking up in terms of rail proximity may in fact be units that have lower amounts of parking, and that's highly influential in terms of how much people drive.

Madeline Brozen 00:18:22
So you mentioned there was a lot of controversy when this paper came out because you know it was funded by the state and there were concerns about how, whether the gentrification is bad or good and kind of the balance between VMT and gentrification. So I don't know, I'm just curious kind of your thoughts about how to be a researcher working with public funding and thinking about having to go through the kind of academic process but also working with policymakers. You made the point, the data is the data, but what do those conversations really look like?

Dan Chatman 00:18:55
The way that I look at it is, look, in academics, we actually we occupy a very specific role in society and, for those of us who are in planning departments, we are often in a position of feeling a need to advocate for certain particular interests and even certain ideas, and I think it's little. It's tricky to be in that position because, in fact, the value added that we bring as researchers is, in fact, what do the data say? What can we learn from the data? That is, new information, helpful information, conclusive in some way, good evidence. We're out there trying to seek evidence and then we get into the realm of making evidence-based arguments, and I think our responsibility as academics is to make sure whatever arguments we're making, that they're evidence-based, and I don't believe that everyone in the academy, particularly in planning, does that. I think that it's easy to have opinions based on our experience as researchers and as practitioners, and sometimes we tend to go with those opinions, even though we may not actually have explicit data that support those opinions in all cases. Anyway, that's something that I always lead with when I'm talking to policymakers who are funding the work is that what we're trying to do is find out what is happening in the world. We want to understand it correctly, and the reason we want to understand it correctly is because we want to make interventions that actually make a difference. So, if we inaccurately believe that we're going to reduce VMT by insisting that all housing that does happen near transit is restricted to only affordable housing, I'll give an example of something that would be controversial if I were to talk about this, if I were to say, okay, you're considering a policy that is only going to permit affordable housing to be built near transit and, as a result, what you're going to do is you're going to permit very many fewer housing units will be developed there, so very many fewer housing units are developed near transit. It this research would suggest that the VMT reduction would be significantly lower. You wouldn't get nearly as much of an environmental benefit, and it might be that you also wouldn't get nearly as much of a housing benefit, by the way, but does that mean that you should necessarily say that gentrification is a good idea? I don't think that's what this means, but we want to understand the ways in which goals for affordable housing might be different than goals for climate benefits, for example. So I do happen to think, though, and other work that I've done with Carolina Reid and Mike Manville again, and Elisa Barbour and Tamara Kirshner in a report that we came out with for ITS, for Institute of Transportation Studies that was kind of sponsored by the California State Transportation Agency, looking at the links between affordable housing and housing issues and travel. I think that these are actually, I think the research suggests that permitting greater development of housing in infill areas and in near transit would both reduce housing cost burdens and have positive travel benefit outcomes for the region and for individuals. So I happen to think that the research really supports that. But that isn't necessarily congruent with what particular advocates in particular areas want to do either in the transportation side or the housing side.

Madeline Brozen 00:22:02
So I think what you're really getting at is this tension for a number of different things, right? So there are some almost truths of good things, quote unquote, in planning. You know that, like we should be doing good things, and what you're saying is that, as an academic who really cares about evidence-based planning, is that sometimes you have to maybe push back or just make sure that you have the actual evidence and the data is supporting that the good things are happening, and so that's kind of one thing, a really important tension that I think is useful to call out, and the other part is that there are tradeoffs. Like I think that—

Dan Chatman 00:22:36
Sometimes there are trade-offs.

Madeline Brozen 00:22:38
Sometimes there are trade-offs and I think the public has a pretty bad time of having honest conversations about trade-offs and their tensions, especially between new housing and where it goes and how we want people to get around. But so, talking about that other work that you were mentioning, it sounds like it doesn't. There are trade-offs but also there are some mutual benefits.

Dan Chatman 00:23:03
I think so, and I think it gets pretty specific into in ways that are not necessarily congruent with the goals of, you know, individual funding agencies, intentions or how the state is how the state of California is understanding where it should make transportation investments, or the way that the state of California understands how it should be modifying housing policy. And as an example of this, I think this paper is about rail, but I think that the focus on rail is an example of this oversimplification that yields benefits that are much smaller than they would otherwise be if we could think about policy more broadly, not just make it be about transit, but make it be about accessibility more generally and make it be about sustainable accessibility more generally. So transit is a very small share of all trips in California. Walking has a much higher share. Reduction of driving by five or six percentage points would probably swamp the environmental benefits of transit, doubling transit, but that's not how we think about things and I think that academics have a responsibility to try to look at data and just to show what's actually happening. I'm not trying to advocate for any particular policy when I say these things. I'm just trying to make sure that we're looking at evidence and not falling into the trap of taking a side in a pre-existing policy conflict about, let's say, whether or not we should be taxing people to fund transit, because I think that's what it comes down to. What happens is you have a particular policy idea out there and then we're meant to sort of say yay or nay on that, and I think academics should be saying, well, you know, here's what the benefits would be. Can we compare what those benefits would be to another intervention, given what the data patterns show about travel patterns and about travel behavior and about housing?

Madeline Brozen 00:24:47
So in this work, kind of coming back to this paper, and we'll talk about the other one you mentioned too, is that you know you found that once you control for population density and employment density, rail proximity itself may lose statistical significance in predicting how much people drive. As you mentioned, this was a result that was also in a prior paper of yours that I come back to all the time. You were not set up in time to doing that. That other paper is Does TOD Need the T? And that paper argues that there are factors beyond just the rail transit itself that makes the difference. In that work you point to parking availability, smaller housing units and more destinations within walking distance. You kind of mentioned this. It's the accessibility, and especially about grocery stores I just kind of think about if there's a recommendation is like, if you care about reducing driving, put a grocery store in your TOD right, like that would make the access for people so easy.

Dan Chatman 00:25:41
The grocery store question is a fun one, because most people can't manage to do groceries without some form of assistance carrying their stuff, and that's what people have a hard time with. And grocery stores don't usually survive without a sufficiently large customer base to draw upon, because customers expect grocery stores that are sufficiently large to offer product variety and also not have high costs and everything else right. So we have these fundamental tensions in the market that we're not necessarily going to solve with transportation policy. And I do think it's true that if you have a grocery store near you're less likely to drive to it, but there's some evidence that some people just might drive more frequently to it. So if you're looking at cold starts, you might be worse off. And then the grocery stores. Since we have grocery stores that are routinely exceeding 60,000 square feet, these, you can't have a grocery store everywhere, right? It doesn't work that way and therefore people are going to be traveling to it and therefore it's going to be harder for that to happen outside of a vehicle in the American context. So there are examples like that we have to kind of grapple with, and there may not be a simple policy that's going to solve the problem, because we can't in fact mandate grocery stores on every block.

Madeline Brozen 00:27:03
I mean I think that paper also kind of points to that it's the stronger predictor of driving. Less, we have to remember, is about having less cars. Right, it's really the auto ownership side of the equation. If you want people to drive less, the most straightforward way is to reduce the number of cars they own, having a household with one car for multiple people or zero cars if it's not actually punitive to people's life. So you know, you kind of mentioned to this already. But what does this finding mean for TOD policy if it's really more the density and the parking and the mixed use rather than the rail station that lead to VMT reductions?

Dan Chatman 00:27:42
So a couple of things. There are lots of interventions, policy planning interventions, that have created conditions that increase people's driving, and one of those is obviously the off-street parking requirement and the on-street parking. The way that we manage on-street parking and the fact that we subsidize it so much that people essentially don't take into account parking costs at all when they're deciding how to travel, and because parking cost in real social terms is high, this means that people are driving way more than they otherwise would, and that's just not socially efficient, it's not equitable, it's not a desirable outcome. And that's different than saying that we should think we should take people's cars away. It's more like saying, well, what's fair, what's efficient, what's the right thing to do for everyone as a group? And one of the answers to that should be something along the lines of please stop requiring parking and please also stop all these restrictions on density. We have lots of restrictions on density and in fact in neither of those papers do I get too explicitly into the issue of scale, but another work of mine and others that is really important in this area. The question of scale of the built environment is really shown to be very important and we have a report out a California Resources Board report out with Marlon Boarnet and Seva Rudinansky and Andre Kamendon and Brian Lee Snyder and John Atkins and Kiran Patel, I think I've got everybody on that who worked with me, looking at more recent data for California from the 2017 NHTS, the most recent year that we have good data for California for, and showing that the most important predictors of VMT are measures taken primarily at areas of larger than one mile radius, like the two mile radius. And that's really important because, if we think about land use policy and TOD policy specifically, it's very site focused, it's very narrow, and what we really need if we want to reduce VMT is we need bigger, denser cities that go in many directions and they're still dense in every direction for a long way. And why does that make sense? Because the market that we live in, the grocery shopping market, the everything market, is all about proximity to lots and lots of different things: parks, transit itself, and we get greater and greater levels of density that enable alternative modes and that enable shorter driving trips. And also because of the fact that less parking is typically specifically provided in dense places, because part of the density is enabled by a reduction of parking. It makes the cost of driving hue a little bit closer to its real cost, its real social cost. So I think all of those things together, and I'm going outside the papers that you're citing to talk about other stuff, but I do think this issue of scale is super important and our policies do not work at this scale. Our policies for sustainability and transportation, land use, do not primarily think about it as a problem of the entire city and it kind of is. It's kind of the problem of the entire city and it's particularly a problem of the biggest cities, the New Yorks and Chicago's and San Francisco's of the world, who do continue to have significant constraints on densification in their cores. And you wouldn't think that because they're already really dense. But they would be a lot denser and if they were, we would, we'd have way more sustainability and we'd also have a lot of other benefits, including more affordability.

Madeline Brozen 00:31:00
I also want to pick up on something you mentioned, that if we want to reduce vehicle miles traveled, it doesn't always have to be about mode switching, like you mentioned, that things being closer together or the you know the two mile, means driving trips are shorter, and maybe it's just a simple math. But I was like, oh, I think we get so focused in the mode switch paradigm that it's like, well, people just drive less. If things were just closer, that would still be good.

Dan Chatman 00:31:25
And the other thing that is worth mentioning there is the potential for carpooling goes way up. So carpooling has been the big. If you want to look at the biggest decline in the mode share for alternative modes in the commute, in the census data you're going to find, over the last 20 years it's carpooling. Carpooling has just dropped off completely. If we had more carpooling, I made a point earlier about if we decreased driving by 5%, we would probably swamp the environmental benefits of transit doubling by transit. I'm making that up. I don't know if that's true, but it's something like that right. You could do the same kind of analysis for carpooling. If you're able to double the carpooling rate, you would get way more benefit in terms of VMT reductions than you would for most other alternative mode options. So, yes, exactly, reduction of driving and also the extent to which, when you have a shorter trip, it's more likely that you're able to carpool, because it means that you have destinations and things that are closer together and so drop-offs and pickups are more likely to be possible. There is a kind of a positive cycle that's possible when you bring everything, origins and destinations, closer together through land use policies over a period of time.

Madeline Brozen 00:32:33
So I know you talked about being a real data-driven person. I am going to ask you a speculative question so you can punt on it if you like. But based on this work, would you expect similar reductions in VMT if higher-income residents moved from lower-density areas into dense non-rail areas? So, for example, in Minneapolis, where I'm from, there's rail but it's really downtown in the university and part of a weird industrial corridor that's not dense. It doesn't really make that much sense. But there are other dense parts of the city that don't have rail. Or in California are kind of more mid-sized cities or coastal towns, Santa Barbara, Ventura. Their downtowns are the densest part, but they don't have rail. So if you built new housing in these areas and higher income people moved in, would you expect similar results to your work in these rail areas?

Dan Chatman 00:33:22
I mean, I absolutely would, and there's a couple of reasons for that. One of them is, I just already alluded to, the fact that these larger radii of built environment factors really matter like a larger scale, like a two mile radius or something, and if you're in a downtown area, you're usually getting way better metrics on those fronts in terms of, like, just the general surrounding area for a long distance. That's one reason. The second reason is sort of building on something in that paper that you mentioned earlier: Does TOD Need the T?, the 2013 paper in the Journal of the American Planning Association, and in that paper, what we found was okay, rail proximity, and this is New Jersey data. So we're talking about New Jersey, which has really good rail. It has actually relatively good transit accessibility compared to most other parts of the country. Is it good comparison to Europe and Asia? No, but it's better than most parts of the United States. And what remained statistically significant in those data in terms of predicting the number of grocery trips, the commute share and the auto ownership shift, those are the three variables that we studied in that paper, was bus stops. That was our measure of bus accessibility, and the thing about downtowns is they may not have rail, but they typically do have pretty good transit accessibility anyway. And so for those of us who are transportation scholars and transportation geeks, or whatever you want to call us, we tend to think about transit not in terms of just rail proximity. We tend to think about it in terms of transit accessibility. And so if you looked at transit accessibility in downtown Minneapolis, what you're going to find is it's pretty good. It's better, in fact, than an outlying rail stop at the end of the LRT in Minneapolis-St Paul. So that's part of the reason why you would expect greater VMT reductions in those places. And then the other thing that needs to be said is that, if you're in downtown, parking is a lot harder to do and people can kind of get away more likely to be able to get away without owning a car, or maybe not without owning as many cars, and when you don't own a car, that's a very good predictor of you're not driving as much, absolutely.

Juan Matute 00:35:18
So these VMT effects, these other effects may be true, but people are really concerned in California, especially about displacement and gentrification, and so the second half of your paper is where you move from correlations to a scenario analysis, trying to estimate the net regional VMT impact of real gentrification happening in specific California census tracts between 1990 and 2013. And this is the piece that the previous research had been missing. Can you walk us through how you did this?

Dan Chatman 00:35:54
I mean it's kind of simplistic, it's pretty basic. It says, okay, let's be kind of binary about this. We can just divide households into two categories and then we can look at low-income households and higher-income households and then we can look at near and far away from rail. So we're just trying to. It's almost a thought experiment. It's saying, okay, what's the relative level of VMT that we would expect from a low-income household near and far away from rail? And that's from the models, it's from data from the CHTS and the NHTS. Yes, and so we show both of those data sources and those estimates. And if you apply the low income average to the low income group and the high income average to the high income group, and you do that for both farther away and near rail, and you just assume that if you're away from rail, that you're living in some average place that's not near rail. So that could be anywhere in the metro area, right, but it's the average place in the metro area. And if you do that, what do you see in terms of expected VMT changes? And what we show there is that you expect for the most part in every census tract that we looked at reductions in VMT and sometimes they're small, sometimes they're a bit larger, and the reason that you're getting a reduction in VMT in each instance is because, essentially, what you're doing is saying, let's assume that this process didn't happen. We can see that it did between 1990 and 2013. We can see how many lower-income households, what the change in the lower-income households was and what the change in the higher-income households was. We can assume, if it didn't happen at all and those people, instead the increment, all lived outside of transit, outside of rail. What would the aggregate VMT change be? And, for obvious reasons that I've already sort of spoken to, because of the fact that in each of these areas there was an increase in households, it means that in every single case there is a VMT decrease, and this is primarily driven by the fact that in each of these census tracts there was an increase in the population, but it's also partly driven by the fact that there are measurable differences in the data between VMT levels for people who are either low-income or higher-income living near rail in comparison to not living near rail. So, in that been done, it hadn't been done explicitly in this way or at all as far as I can see, and it just is illustrating the two factors at play. One of them is, you know, how much less do people drive when they're near these rail stations? And the second one is, how much is population increasing in these places?

Madeline Brozen 00:38:26
So there's an argument that gets made that market rate upzoning near transit is what causes gentrification in the first place. I'm not entirely sure that's borne out by the evidence, but how do you think about that tension?

Dan Chatman 00:38:41
I will say this, I do think that there is a danger of gentrification. So let's do a couple of things here. First of all, gentrification. What does that mean? So the original meaning of gentrification was something along the lines of, are higher income people moving into a place? Gentrification then got lumped in with this concept of displacement at some point, both in the academic literature and the discourse in the media. But these are different things. So more high income people moving in is a phenomenon that happens, and then displacement is something else that can happen. The interesting thing about upzoning is upzoning is more likely to result in gentrification without displacement in comparison to not upzoning, and the reason for that is, I'm making it, this is a generalization. There's going to be exceptions to every rule. But if you don't upzone, what that means is that you're saying, okay, if people want to live there because it's becoming more popular or because of increasing incomes, then the way it's going to happen is that the existing people living in those units are going to have to be displaced by the people who are willing to pay more for those units. So the idea that upzoning causes displacement, let's set aside gentrification for a minute, the idea that upzoning causes displacement is problematic. It has very little evidence to support it. But, however, upzoning can absolutely cause gentrification. It can absolutely cause people coming in of higher income, and the reason is because, number one, you're not going to upzone typically, except in a place where you know, oh, there's market demand. Number two, if there's market demand, that means that there's people with money wanting to live there. And if you're going to build new housing, new housing is more expensive than old housing. New housing is going to typically be occupied by people who are of higher income. This is the issue that I have with the notion of affordable housing as being something that we build. I think that affordable housing as something that we build is a losing strategy for providing affordable housing. It needs to be much broader than just new housing, but that's sort of as an aside. So upzoning is more likely to cause gentrification than displacement. And will it cause gentrification? Yeah, pretty much, especially given the following very important phenomenon in the state of California, which is everyone's obsessed with TOD as a strategy. What does that mean? It means you're only upzoning near transit. Well, that's dumb. We should be upzoning much more broadly and we're starting to do that a little bit in the state. But upzoning should be happening much more broadly than just near transit. In places, by the way, in places where there is so much transit that upzoning near transit means upzoning generally across the metro, like San Francisco County or San Francisco, same thing. It's less of a problematic concept because everywhere is TOD, but on the other hand, the levels of upzoning are probably not high enough, and so you have these controversies happening in San Francisco right now, where there are these developed. There's these several specific examples of owners of existing grocery stores who want to develop large towers on top of them and all the neighbors don't want it. The fact is, neighbors don't want more housing, so that's a problem.

Juan Matute 00:41:37
Well, San Francisco is always an interesting case. So the mission district track that you studied actually gained both low income and high income households, so no net displacement but significant gentrification.

Dan Chatman 00:41:50
During that period of time.

Juan Matute 00:41:52
During that period of time, 1990 to 2013.

Dan Chatman 00:41:55
But definitely gentrification was happening. But what I always come back to it was, okay, well, let's say that we had stopped that. What if we had stopped new development happening there? And I think the answer to that question is, well then, probably there would have been more displacement because I think the demand was there for that housing and over time, especially with renters, they're going to be displaced anyway. So that's the relationship, I guess. This is one of the reasons why I think the data show the potential synergies between broadly probably permitting more housing development and reducing travel. I think those things actually go together for the most part in California cities where we have a lot of demand for development.

Madeline Brozen 00:42:35
So, yeah, I think what you're saying broadly is that you know, when we talk about that density and parking matter more than rail access, and that upzoning near transit is a way to reduce VMT but also and—

Dan Chatman 00:42:51
And also reduce displacement.

Madeline Brozen 00:42:52
Also reduce displacement.

Dan Chatman 00:42:54
That's what I'm hoping is the headline here. I'm hoping the headline is "Upzoning Reduces Displacement." I love that. That'd be a great headline.

Madeline Brozen 00:43:00
Well, you know, I think that what you're saying is that it's just an argument for building dense housing, full stop, right.

Dan Chatman 00:43:06
Well, it's an argument for permitting it to be developed. Let's put it that way. I think as planners, I mean, I teach in a planning school and so I teach students, a lot of master's students, in planning, and I think we all think about what should planners do? And a lot of it is. But the answer to that question is often, well, we should do the things that we know need to happen. And I think our answer to that needs to be more subtle. It needs to be, well, we need to adopt policies that don't interfere with those things happening, and we need to identify the role of the public sector. What is the proper role of the public sector in encouraging those good things to happen? But we need to define good things as being broader than just, let's say, reducing driving anywhere we can like. We shouldn't try to reduce driving everywhere we can. There's lots of examples where people are best off driving and from a social perspective, they're best off driving. So the question is, what do you know, what do we do? And we're not going to be able to build dense housing everywhere because not everyone's going to support it. So you know, the Turlocks of the world may want to have a TOD, but there's no market support for it and it's not clear that we should be spending a lot of extra money to make sure that we build something dense near their rail station. Not clear that's a good use of limited public funds? You know what I mean. So that's. What I'm getting at here is that it's about getting a clearer sense of what are the impediments to market, provided improvements to solve problems like affordable housing and transportation accessibility, and then what's the public's role in fixing the problems that the market doesn't fix. And I think we've done a lot of pushing hard against the market and spending a lot of energy at that, and I think that's energy not as nearly as well spent as perfecting the market and solving market failures, basically. So in that sense it's a pretty basic argument, and I don't mean to say that I am a doctrinaire market economist or believe in market solving problems. I think that we have very clear theory about circumstances under which markets don't solve problems, but we're not exercising that as planners for the most part. Instead, we're trying to come up with our own vision of what we think the city should look like, and then we're trying to legislate that. I think that is part of the issue that we have in actually making progress on these problems: the problem of sustainability and the problem of affordability.

Madeline Brozen 00:45:20
Well, and I'm not sure that we're even that good at trying to make it the city we want, because it's this grand compromise of what is a city we could have if we are willing to hold 10 to 15 public meetings and take everyone's personal little opinions into account.

Dan Chatman 00:45:38
I mean absolutely. That's tough, right. I mean, planners have a really hard job, especially when you're referring to the idea of a truly facilitative, inclusive planning process that is meant to reflect a lot of different people's interests. That's really difficult to do, but the ironic thing about that is what we think about as the ideal facilitated planning process is, in theory, what a market provides, because markets involve lots and lots of people making decisions about things and expressing their preferences. So how can you make that happen in a way that actually is using those ways that people behave and seek out their own interests and preferences, where we're trying to facilitate them, meeting those needs and preferences, while acknowledging, not everyone's a good judge of their needs and preferences, while acknowledging our own? We know that we don't always make good decisions for ourselves, but we don't, as planners, necessarily have the ability to solve that problem. So what we're trying to do is do the best we can, given our limited potential for intervention and our limited funds that are available. What planners have been historically good at doing is saying no. The tools that they have are blunt and they involve saying no, and we need to move beyond that, because when I say saying no, what I mean is we can stop things from happening. We have the tools to do that.

Juan Matute 00:46:56
So one way California in particular is moving past, that is, AB 2097, which reduces or eliminates parking requirements. Two episodes ago, we had Amy Lee of UC Davis, who's researching the implementation of this bill, which restricts cities from requiring parking in areas near transit, and so, given your research, do you think that this is an effective strategy for both densification and reducing driving?

Dan Chatman 00:47:29
I have a very specific and obvious complaint about this, which is that it's ridiculous that this is only for places for development projects within a half mile of transit. When I say it's ridiculous, I mean there's no data to suggest that this should be limited to those places. There's no reason why it should be limited to the places that are within a half mile of public transit, other than that, absolutely it shouldn't be. I mean, the notion of imposing minimum automobile parking requirements is, I guess, to borrow one of the late Don Shoup's favorite metaphors, like you know, like the use of lead to treat sepsis and wounds. It's like it, you know, kills the patient, solves a local problem and it causes a regional, you know it kills the region. So we've been doing that over and over again. Yes, we shouldn't be doing that. Finally, we have a bill that's going to stop it within a half mile of transit. If you looked at the fraction of land that this applies to in the state, I don't know what it's going to be. It's going to be minuscule, 6% at best.

Madeline Brozen 00:48:25
And if, yeah, if anyone's listened to this episode and didn't catch the previous one, you could also get into. Like you know, that 60% really depends a lot on how you get into the exact definitions, and it's just—

Dan Chatman 00:48:36
Of available land. Yeah, right, yeah.

Madeline Brozen 00:48:38
Exactly, it's an exercise in compromise.

Dan Chatman 00:48:41
It is. It is, I just think, as academics. Here's what I notice. I notice academics understanding limitations and then starting to conform to the limitations, and I think our job is to say this makes no sense. To put it another way, I think our job is to make it clear the extent to which it may be the case that it's only possible to do this, but it's kind of crazy that this is the only possible thing. Yeah.

Juan Matute 00:49:05
Interventions that are made based on a misunderstanding of the world or a lack of understanding of the world are likely to have unintended consequences.

Dan Chatman 00:49:13
I mean Maddie's bringing up the point that it may be what's politically possible and that's true, and so I think my lucky star is every day that I don't have to think within those bounds. What I notice is that people who have to think within those bounds end up not thinking outside those bounds, and then that's not so helpful. And academics should be able to think outside those bounds. That should be part of our role, right? So I suppose you didn't expect that you were talking about the bounds of what academics roles would be in this podcast, but inevitable.

Madeline Brozen 00:49:44
No, I think it's good. I think that we want to both talk about what people found and the relationship between academia and practice, and part of that is to push what we think is imaginable and, to your point, just call things ridiculous that are ridiculous. I think that's one of many great things about Don Shoup is he just kept beating the drum of saying how ridiculous things were, you know?

Dan Chatman 00:50:06
He kept beating the drum, yeah, and he did it with humor, and people started to realize, yes, this is ridiculous. Eventually everyone realized it was, and yet we're still only allowing public, we're still only allowing the off-street parking to be relaxed only within a half mile of transit.

Juan Matute 00:50:24
And he did it for 20 or 30 years before he released his book.

Dan Chatman 00:50:29
Before he made any impact and before he got any traction, and so, yeah.

Juan Matute 00:50:31
You took his course before he released his book and it was probably very different then.

Dan Chatman 00:50:36
I was. I was one of the people he thinks in the preface. Yeah, no, I actually didn't take his course because I was not a master's student in that program, I was a PhD student and so I never took a course from him. But he was on my committee and it was one of the many students who you know did the multiple edits, argued with him about his puppies under a blanket metaphor. I hated that one.

Madeline Brozen 00:50:57
It must have worked, because I've heard a lot of Don Shoup metaphors in my 15 years at UCLA and I haven't heard that one.

Dan Chatman 00:51:02
No, it's there. It's there, but no, it's not very popular because it's a dumb one. It's there, it's in the book. It's in the book and in one of the papers. Yeah.

Juan Matute 00:51:11
Well, if anybody's made it to this point in the podcast, they are a true fan of the idea of research, answering questions that are maybe difficult or uncomfortable to answer, or your research in particular. So I was wondering if there's something that you couldn't quite study in this paper or in other papers that you think should be studied or that you would like to be able to test.

Dan Chatman 00:51:35
Well, one thing that I'm going to be working on with the co-authors I mentioned earlier, in particular Marta Gonzalez, is getting better and better data on activity. I think our data aren't great. I mean they're based on travel diaries, they don't look at data across a whole week. They don't look at data, they don't look at everywhere people go, because people fail to report it, and so I think we're, over time, going to get some more accurate data on how much people travel and how much they may substitute between different days of the week. And so, with a doctoral student, Yaga Liu, here at UC Berkeley, we're looking at data from the Bay Area Travel Survey, which is a week-long travel diary, which enables us to look at, for example, if people are working at home, does that mean they travel a lot for non-work purposes, either on those work days or on other work days, because they're more likely to want to get out and about because they don't have a commute? Those sorts of questions that are increasingly important, as we really are changing the way that we organize our lives in the post-pandemic world. So those kinds of questions, there's lots of questions that are really interesting. The fact is that we work a lot in policy and in planning without sufficient data on things, and working with data is a challenge. It actually takes a lot of attention to detail, a lot of false starts, a lot of debugging of the code and working with data that are not perfect and trying to figure out how to impute information. So I am excited to be working with new forms of data over time that get us better answers to the questions about what actually happens when you do something over time and can you actually observe people's changing behavior. Getting to your residential self-selection question earlier, one of the ways of getting at that is to be able to look at experiments or look at changes happening and looking at before and after behavior. We typically don't have data from people before and after some change. If it's a new bike lane on a road, can we look at the households living nearby and any change in behavior before and after? We're starting to be able to eventually get to that point and that's an exciting thing to be able to see and get better answers on this critical question of the extent to which our public investments make a difference in transportation sustainability.

Madeline Brozen 00:53:40
Well, Dan, it was a genuine pleasure to have you here today. Thank you for being on Road Scholars. We'll have links to the papers we talked about and some other resources in the show notes, and we'll catch you all next time.

Dan Chatman 00:53:53
Thanks, it was fun.

Madeline Brozen 00:53:58
You can read more about Dan's work on our website, lewis.ucla.edu, where the show notes and transcript can be found. Thanks again to Transfers Magazine for their support; you can find them at transfersmagazine.org. The Lewis Center is on BlueSky and LinkedIn, and thanks for listenin'.