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The impact of the residential lead paint disclosure rule on house prices: findings in the American Housing Survey

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Abstract

In an effort to reduce lead paint exposure in old homes, the residential lead paint hazard disclosure rule (Title X) was enacted in 1996 in the USA, which requires house sellers to disclose known lead-based paint hazards to buyers. The policy is designed to induce individuals’ improved maintenance behaviors through environmental information provisions. Minimizing the impact on housing prices is also the key component of the policy; a decline in the price of old homes because of the policy may lead to higher occupancy rates of the targeted low-income groups into lead paint risk homes. To explore the issue, this study examined whether the lead paint disclosure rule lowered values of old houses using the American Housing Survey data from 1993 to 2005 with the repeat sales method. The findings show the policy did not lower prices of old homes, as it intended.

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Notes

  1. The AHS data used to compose the sales-based panel data are from 1993 to 2005. However, the final sales-based panel covers sales from 1985 to 2005 because purchase years of surveys trace back to 1985, not to the earliest survey time point, 1993.

  2. Houses built before 1978 and after 1978 are systemically different in many aspects, which implies a serious selection problem. However, because an equivalent control group was not readily available (nation-wide introduction of Title X), this study tried to adopt a non-equivalent control group approach instead. Although this study tried to partly address the issue with control variables (e.g., maintenance cost variable), it is a key limitation of the analyses.

  3. The repeat sales model assumes that the neighborhood characteristics are time-invariant. It is one of the major weaknesses of the standard repeat sales model. It would be highly desirable to identify the location of each house and to include neighborhood characteristics such as school quality. However, the public-use AHS does not reveal the detailed location of each house. Thus, this study estimated the model based on the assumption that neighborhood characteristics are constant.

  4. The repeat sales model in this study is a similar form with the difference in difference model. While the difference in difference model is often used refer to the design to estimate a treatment effect with pooled cross-sectional data, the repeat sales model specifically puts an emphasis on tracking the same houses for pre- and post-groups.

  5. I treated the houses that sold more than twice as multiple repeated sales pairs. Those houses must have a different error structure. However, this study followed a general repeat sales model approach without further consideration of the different error structures. This is one of the limitations of this analysis.

  6. Previous studies have found age-related depreciation at a non-linear rate (Shilling et al. 1991; Lee et al. 2005).

  7. Among the baseline sales-based panel data with 7,135 houses, the repeat sales model dropped top-coded observations, non-single houses, observations for which the annual nominal rate of house price appreciation is greater than 50 %, and observations with unusually high levels of maintenance (maintenance expenditure more than twenty 5 % of the purchase price), as done in Harding et al.’s study. The annual maintenance expenditure variable is constructed by adding annual routine maintenance expenditures and project-specific costs.

  8. The weak instruments test result (Stock-Yogo test: F statistic 19.042 > 10 % critical values 9.08) rejects the null hypothesis of weakness of the instruments. The over-identification test results (Hansen J test: Chi-square statistic 1.670, p value 0.433) show that the instrumental variables are valid.

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  1. Department of Public Administration, Yonsei University, 134 Sinchon-Dong, Seodaemun-Gu, Yonhee Hall 213, Seoul, Korea

    Hyunhoe Bae

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  1. Hyunhoe Bae
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Bae, H. The impact of the residential lead paint disclosure rule on house prices: findings in the American Housing Survey. J Hous and the Built Environ 31, 19–30 (2016). https://doi.org/10.1007/s10901-015-9441-x

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