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The Journal of Real Estate Finance and Economics

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01-06-2012

The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study Using Propensity Scores

Authors: Anupam Nanda, Stephen L. Ross

Published in: The Journal of Real Estate Finance and Economics | Issue 1/2012

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Abstract

We examine the impact of seller’s Property Condition Disclosure Laws on residential real estate values. A disclosure law may address the information asymmetry in housing transactions shifting risk from buyers and brokers to the sellers and raising housing prices as a result. We combine propensity score techniques from the treatment effects literature with a traditional event study approach. We assemble a unique set of economic and institutional attributes for a quarterly panel of 291 US Metropolitan Statistical Areas (MSAs) across 50 US States spanning 21 years from 1984 to 2004. The study finds that the average sales price of houses in a metropolitan area increases by an additional 3 to 4% over a 4 year period if the state adopts a Property Condition Disclosure Law, which is consistent with approximately a 19 basis point or 6.4% reduction in the risk premium associated with purchasing owner-occupied housing. When we compare the results from parametric and semi-parametric (propensity score) event analyses, we find that the semi-parametric analysis generates moderately larger estimated effects of the law on housing prices.

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Anti-lemon laws in the automobile industry provide an example of an alternative approach for addressing this problem (Shaffer and Ostas 2001).

Easton v. Strassburger (152 Cal.App.3d 90, 1984) was a California Appellate Court decision that expanded the duty of realtors and the grounds for realtor negligence in selling faulty homes.

See Lefcoe (2004) for a detailed discussion of the implementation of seller disclosure laws, and Pancak et al. (1996) for a discussion of real estate broker incentives concerning the adoption of seller disclosure laws.

For discussion, see Fuerst (2005).

The economic controls are standard in the literature on housing price volatility (see Miller and Peng 2006). We include the state-level institutional characteristics to control for the fact that such variables might be correlated with both law adoption and unobservables that correlate with housing price appreciation, but naturally we do not expect these controls to have a causal effect on housing prices.

See Todd, P.E. “A Practical Guide to Implementing Matching Estimators” Unpublished Manuscript, for a discussion on this and other matching estimators.

The hazard function can be represented by a standard normal cumulative distribution function.

$$ \log it\left( {P_1 \left( {w_j; \beta } \right)} \right) = \log \left[ {{{P_1 \left( {w_j; \beta } \right)} \mathord{\left/ {\vphantom {{P_1 \left( {w_j; \beta } \right)} {\left( {1 - P_1 \left( {w_j; \beta } \right)} \right)}}} \right. } {\left( {1 - P_1 \left( {w_j; \beta } \right)} \right)}}} \right] = w_j^{\prime } \beta $$

In equation (5), the underlying assumption is that the probability $ P_1 \left( {w_j; \beta } \right) $ approaches zero and one at the same rate i.e. the link is symmetric. However, as explained before, if a disparate treatment exists, an alternative is complimentary log-log or the proportional hazard link function, which is:

$ - \log \left( { - \log \left( {1 - P_1 \left( {w_j; \beta } \right)} \right)} \right) = w_j^{\prime } \beta $

or, $ P_1 \left( {w_j; \beta } \right) = 1 - \exp \left( { - \exp \left( {w_j^{\prime } \beta } \right)} \right) $with $ P_1 \left( {w_j; \beta } \right) $approaching one faster than zero. See Clarke et al. (2009).

Obviously, many complex extensions exist to the traditional proportional Hazard model. However, propensity score estimation is very robust to misspecification in the link function and so is typically conducted with very simple link functions such as the proportional hazard model in our case and logit or probit in traditional applications (Dehejia and Wahba 1999).

Traditionally, the score is used to divide the sample into equally spaced intervals or bins, and within each bin a test is conducted for whether the average propensity score of treated and control units do not differ statistically. If it differs, the interval is split again until the condition is satisfied. With Kernel Matching all treated are matched with a weighted average of all controls with weights that are inversely proportional to the distance between the propensity scores of treated and controls.

The estimation is carried out in the common support region. Common support refers to overlapping distributions of their characteristics. Another important advantage of propensity score approaches compared to traditional regression-based methods is that regression hides the common support problem, because it does not quantify the similarities (or dissimilarities) between the two groups.

Pancak et al. (1996) lists the states, which adopted the disclosure law until 1996.

Proxy GMP=GSP*(MSA population/State population).

Since linear interpolation takes two yearly values and fits a straight line while projecting the data in between, it is generally less accurate than other polynomial based methods. So, we apply a cubic spline interpolation method, which uses the data point value along with the first and the second derivatives at each surrounding point to interpolate. When we compare the results with interpolated quarterly data with the actual yearly data, the qualitative results do not differ.

The variable set and description are taken from Nanda (2008).

When disciplinary actions figure is missing or zero, we take the average of the figures within a 1-year range. When total disciplinary actions figure is missing in ARELLO reports, if available, we take the sum of the figures under different categories of disciplinary actions, or, we take the sum of the actions by consent and number of formal hearing as total number of disciplinary actions (this is the case until 1986). Then we take sum of disciplinary action and formal hearing from column of complaints resulting in some actions. Both of these are expected to provide a number of complaints that are sufficient to attract legal attention. This is typically the case with Arizona and Hawaii for 1984 to 1986.

Ideally, the percentage of licensees who are associated with some trade organizations like National Association of Realtors (NAR) could serve as an excellent indicator of the lobbying effort. However, it is hard to obtain this information across the states for the long time series required for this study.

The supervision index is defined as the percentage of active brokers to total active licensees. The assumption is that greater supervision can be captured by greater percentage of brokers to licensees. See Pancak and Sirmans (2006) for discussion of a variable named “experience” with similar definition.

Similar results are obtained using a 6 year event window.

While conducting the yearly analysis, we test alternative specifications for the timing of the law adoption. Since we know the effective day of the mandate, we could assign the corresponding year as the adoption year. However, one could argue that if the effective date falls in the last two quarters of the year, the bulk of home sales has already taken place. Therefore, the effectiveness of the mandate really starts from next year. We tried both specifications, and the qualitative and quantitative results are robust to this concern.

See Campbell et al. (2009).

The authors are grateful to Morris Davis for this very helpful suggestion. Note that one could calculate the change in risk premia without resorting to a Taylor series approximation, but such calculations are less attractive because they require explicit assumptions concerning the risk free interest rate and anticipated capital gains, while the expression in the paper can be calculated using the more easily observable rent to value ratio.

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Title: The Impact of Property Condition Disclosure Laws on Housing Prices: Evidence from an Event Study Using Propensity Scores
Authors: Anupam Nanda
Stephen L. Ross
Publication date: 01-06-2012
Publisher: Springer US
Published in: The Journal of Real Estate Finance and Economics / Issue 1/2012
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-009-9206-y

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