Measuring the Purple Line’s Effect on Residential Rents
Spatial Econometrics and Causal Inference in Urban Policy Analysis
February 13, 2024
TL;DR
- PL offers a unique opportunity to explore spatial model specifications in a causal inference context
- ‘Causal’ hedonic models need spatial econometrics
- The spatial weights matrix (\(W\)/
Graph) matters a lot, especially for microdata - Spatial models with Fixed Effect (within) designs have a different counterfactual than DID (between) designs
- within estimator is more appropriate than the between estimator for transit studies (but more difficult data)
- Contrary to prior research the Purple Line probably caused rents to decrease slightly during the announcement phase, and the spillover effect likely extends beyond the station areas
The Purple Line
A 16-mile light rail line that will extend from Bethesda in Montgomery County to New Carrollton in Prince George’s County. It will provide a direct connection to the Metrorail Red, Green and Orange Lines; at Bethesda, Silver Spring, College Park, and New Carrollton. The Purple Line will also connect to MARC, Amtrak, and local bus services.
The Montgomery County Rental Survey
The annual Rental Facility Occupancy Survey included single unit properties (single family homes, townhomes and condos). Inquiries are addressed by the Department of Housing and Community Affairs (DHCA), Licensing and Registration Office (L&R). The survey is required based upon Montgomery County Code 29-51, and says all landlords of rental housing units must participate in the survey, tracking vacancies; turnover rates; average rents; and amenities.
Rent by # Bedrooms
Causal Inference in Hedonic Models
Does the Announcement of New Transit Cause an Increase in Land Rents/Prices?
- Does space matter?
- How should a causal hedonic model include spatial effects?
- DID vs FE designs
- SAR, SEM, SDM, SDEM
- How should co-located units consider spatial weights?
- How much does it matter?
Does the Announcement of New Transit Cause an Increase in Land Rents/Prices?
Here, the treatment is defined by space (inside/outside the station area). Existing work uses classic Difference in Differences or spatial extensions to Difference-in-Differences (SAR-DID, SEM-DID, SDEM-DID).
DID is often the only choice for estimating land price responses to policy, because there is not enough within-unit variation in housing transactions.
When a treatment is defined by space, does DID generate a valid counterfactual?
How Should A Causal Hedonic Model Include Spatial Effects? (1)
A Theory of Spillover in Hedonic Models
“The value of a house at any location is dependent on its counterparts at nearby locations in addition to its structural and neighborhood attributes. The hypothesized spatial dependence among residential structures is determined by \(W\) which is specified in an a priori fashion. The coefficient \(\rho\) measures the absolute price impact of nearby houses on the price of a particular house.”
— Can (1992)
How Should A Causal Hedonic Model Include Spatial Effects? (2)
A Theory of Spillover in Hedonic Models
“As argued in Can (1990), this conceptualization corresponds more closely with the actual workings of the real estate institution in urban housing markets. A realtor will appraise a house given the price history of houses in the immediate vicinity in addition to other substantive characteristics. At the same time, home owners will initiate or forego certain improvements based on the anticipated return on their investment considering housing prices in the immediate area.”
— Can (1992)
How Should A Causal Hedonic Model Include Spatial Effects? (3)
A Problem for Causal Inference
With spatially-interactive observations, the cardinal rule of regression–the Stable Unit Treatment Value Assumption (SUTVA) (i.e. the independence of observations)–is violated (Delgado and Florax 2015).
Classic identification techniques will fail unless spatial effects are included, but different specifications imply different counterfactuals.
…And require additional subjective decisions among competing model specifications and \(W\) specifications