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The Journal of Real Estate Finance and Economics

Erschienen in:

26.04.2019

The Impact of Exogenous Shocks on House Prices: the Case of the Volkswagen Emissions Scandal

verfasst von: Heiko Kirchhain, Jan Mutl, Joachim Zietz

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 4/2020

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Abstract

This study analyzes to what extent the announcement on 9/18/2015 of the VW diesel emissions scandal affected house prices in the vicinity of Chattanooga, TN, the location of the only VW production plant in the United States at that time. We examine the impact of the announcement with house transactions data for the 3 years from 2014 to 2016. We explore a number of alternative methods, including a permutation test, to tie down causation. Our results indicate that the brunt of the negative impact occurred 61 to 90 days after the announcement, with no statistically significant negative effects after 90 days. Although the average price discount for the study area is modest at about 3% to 4.5%, the effect tends to be significantly larger for locations closer to the VW plant. However, geographical distance has a distinctly non-linear influence on the price discount.

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Many studies have shown in this context that house prices react or even overreact to income shocks (e.g., Lamont and Stein 1999; Malpezzi 1999; Ortalo-Magne and Rady2006).

In 1988, all US car production by VW was moved from Pennsylvania to Puebla, Mexico.

We can think of this as an application of earlier work (Glaeser and Gyourko 2005; Moretti 2011) that shows how a drop in employment and income levels is capitalized in lower housing values if the elasticity of the local residential real estate supply is sufficiently low.

Starting with Zietz et al. (2008), an increasing number of studies has been using conditional quantile regression. These include, among others, Mak et al. (2010) and Zahirovic-Herbert and Chatterjee (2012) and Kim et al. (2015) on Hong Kong data, McMillen (2014) on Chicago data and Zhang and Yi (2017) on Beijing data.

First, the data set is reduced to only residential transactions. Second, data points for which the exact geographical coordinates can not be unequivocally determined are eliminated from the data set. Third, transactions with object-specific missing information (i.e. living space, age and price) are excluded. Fourth, outliers are removed, which includes houses with prices below 20 USD per sqft or above 180 USD per sqft.

Separately, we include a landscape specific dummy variable to capture the exposed location of objects on top the “Missionary Ridge”. This locational effect is not adequately captured by the census block group dummy variables as the houses are positioned on the border of two census block groups.

Transaction pattern analysis shows that sales are not evenly distributed within the individual months of the study period. There tends to be a peak of transactions at the end of every month. This pattern is rather typical for the U.S. because both sellers and buyers are reducing out-of-pocket expenses by scheduling the closing at the end of the month.

We select these markets because they are the major cities located closest to the VW plant in Chattanooga for which data are available.

The data are generated with Google Trends (https://trends.google.com/trends/?geo=US). See Varian (2014) for a brief discussion of the use of these data in econometric applications.

This type of model is also known as a trend surface model. See Clapp (2003) for an example application.

This is derived as 10*(-0.010 + 2*0.047*38.5/1000), where the division by 1,000 results from the definition of variable agesq (Table 1).

This “exclusion time” is needed for the mortgage details to clear, for the papers to be written up, and the transaction to be recorded by the registrar’s office (EllieMae 2016; Benefield and Hardin 2015).

See, for example, Mense and Kholodilin (2014) and Bauer et al. (2017)

The first one covers the distance between 0 and 0.05 decimal degrees (4.5km), while the last one covers the distance between 0.20 and 0.25 decimal degrees (18 - 22.5km).

Bi-cubic interpolation is applied for clearer visualization of the pattern of the price effect. A color pattern is used to separate positive (red) from negative (blue) price effects.

Further comprehensive research of US news media, both with national and local focus, has not yielded any other event that could have significantly influenced the house prices in the Chattanooga area during the observed time frame.

See the Appendix for a more formal treatment of the test statistic and its properties.

The regression to generate the u_i is estimated for the entire sample, while test is calculated separately for each combination of geographical and temporal distance.

We re-use the distance buffers employed in Table 4.

For example, for the distance buffer 0.0-0.15, which is shown in Column (3) of Table 4, all observations within this distance buffer are categorized as near, all others fall in the category far.

Deriving the standard errors of Table 6 from the bootstrapped distribution of test assumes that the sample is representative of the underlying population. To the extent that the sample is not representative, the standard errors derived from the re-sampling exercise are biased. This is where the procedure underlying Table 7 comes in.

The apparent uniqueness of distance D = 0.10 is consistent with the results of Tables 4 and 5.

Our maintained hypothesis is typical for the core idea of any permutation test. To establish a valid counterfactual, it intends to eliminate any existing correlation in the data (that relates to the hypothesis to be tested). The resulting counterfactual shows what one would see under the null hypothesis of complete randomness in the relationship under study.

This means that, for each analyzed combination of geographical and temporal distance, a given residual u_i is randomly associated with a geographical and temporal distance. As a result, the expected value of test in Eq. 2 is equal to zero.

See Column (3) of Table 3, but without the variable that captures the linear distance to the VW plant.

We assume for simplicity a single factor model; but the argument is easily extensible to a larger number of factors.

As selected by the indicator variable T_im.

Ahlfeldt, G.M., & Kavetsos, G. (2014). Form or function? The effect of new sports stadia on property prices in London. Journal of the Royal Statistical Society: Series A, 177(1), 169–190.CrossRef

Ahlfeldt, G.M., Maennig, W., et al. (2011). Homeownership and nimbyism: a spatial analysis of airport effects. Technical report, Spatial Economics Research Centre, London School of Economics.

Bauer, T.K., Braun, S.T., Kvasnicka, M. (2017). Nuclear power plant closures and local housing values: evidence from Fukushima and the German housing market. Journal of Urban Economics, 99, 94–106.CrossRef

Benefield, J.D., & Hardin, W.G. (2015). Does time-on-market measurement matter? Journal of Real Estate Finance and Economics, 50(1), 52–73.CrossRef

Case, K., & Shiller, R. (1989). The efficiency of the market for single-family homes. American Economic Review, 79(1), 125–137.

Clapp, J.M. (2003). A semiparametric method for valuing residential locations: application to automated valuation. Journal of Real Estate Finance and Economics, 27(3), 303–320.CrossRef

Du, Z., & Zhang, L. (2015). Home-purchase restriction, property tax and housing price in China: a counterfactual analysis. Journal of Econometrics, 188(2), 558–568.CrossRef

EllieMae. (2016). Origination insight report. Ellie Mae, Inc. https://static.elliemae.com/pdf/origination-insight-reports/.

Ewing, J. (2015). Volkswagen says 11 million cars worldwide are affected in diesel deception. New York Times. https://www.nytimes.com/2015/09/23/business/international/volkswagen-diesel-car-scandal.html.

Gatzlaff, D.H., & Smith, M.T. (1993). The impact of the Miami Metrorail on the value of residences near station locations. Land Economics, 69(1), 54–66.CrossRef

Glaeser, E.L., & Gyourko, J. (2005). Urban decline and durable housing. Journal of Political Economy, 113(2), 345–375.CrossRef

Goodman, J. (1993). A housing market matching model of the seasonality in geographic mobility. Journal of Real Estate Research, 8(1), 117–137.

Harrison, S. (2015). VW: Emissions scandal won’t impact 2,000-job Chattanooga expansion. Nashville Business Journal. https://www.bizjournals.com/nashville/blog/2015/10/emissions-scandal-won-t-impact-2-000-job.html.

Hsiao, C., Steve Ching, H., Ki Wan, S. (2012). A panel data approach for program evaluation: measuring the benefits of political and economic integration of Hong Kong with mainland China. Journal of Applied Econometrics, 27(5), 705–740.CrossRef

Hwang, M., & Quigley, J.M. (2006). Economic fundamentals in local housing markets: evidence from US metropolitan regions. Journal of Regional Science, 46(3), 425–453.CrossRef

Jud, G.D., & Winkler, D.T. (2006). The announcement effect of an airport expansion on housing prices. Journal of Real Estate Finance and Economics, 33(2), 91–103.CrossRef

Kessler, A.M. (2014). Volkswagen to add S.U.V. line to Chattanooga plant. New York Times. https://www.nytimes.com/2014/07/15/business/vw-to-add-suv-production-to-chattanooga-plant.html.

Kim, H.-G., Hung, K.-C., Park, S.Y. (2015). Determinants of housing prices in Hong Kong: a Box-Cox quantile regression approach. Journal of Real Estate Finance and Economics, 50(2), 270–287.CrossRef

Koenker, R., & Bassett, G. Jr. (1978). Regression quantiles. Econometrica, 46(1), 33–50.CrossRef

Koenker, R., & Hallock, K. (2001). Quantile regression: an introduction. Journal of Economic Perspectives, 15(4), 43–56.CrossRef

Lamond, J., & Proverbs, D. (2006). Does the price impact of flooding fade away? Structural Survey, 24(5), 363–377.CrossRef

Lamont, O., & Stein, J.C. (1999). Leverage and house-price dynamics in US cities. RAND Journal of Economics, 30(3), 498–515.CrossRef

Mak, S., Choy, L., Ho, W. (2010). Quantile regression estimates of Hong Kong real estate prices. Urban Studies, 47(11), 2461–2472.CrossRef

Malpezzi, S. (1999). A simple error correction model of house prices. Journal of Housing Economics, 8(1), 27–62.CrossRef

McGee, P. (2018). Remodelling VW. Financial Times Europe, 19, 7.

McMillen, D. (2014). Local quantile house price indices. University of Illinois, mimeo.

Mense, A., & Kholodilin, K.A. (2014). Noise expectations and house prices: the reaction of property prices to an airport expansion. Annals of Regional Science, 52 (3), 763–797.CrossRef

Moretti, E. (2011). Local labor markets. Handbook of Labor Economics, 4(Part B), 1237–1313.

Ngai, L.R., & Tenreyro, S. (2014). Hot and cold seasons in the housing market. American Economic Review, 104(12), 3991–4026.CrossRef

Oden, A., & Wedel, H. (1975). Arguments for fisher’s permutation test. Annals of Statistics, 3(2), 518–520.CrossRef

Ortalo-Magne, F., & Rady, S. (2006). Housing market dynamics: on the contribution of income shocks and credit constraints. Review of Economic Studies, 73 (2), 459–485.CrossRef

Pryce, G., Chen, Y., Galster, G. (2011). The impact of floods on house prices: an imperfect information approach with myopia and amnesia. Housing Studies, 26(02), 259–279.CrossRef

Reichert, A.K. (1990). The impact of interest rates, income, and employment upon regional housing prices. Journal of Real Estate Finance and Economics, 3(4), 373–391.CrossRef

Riley, C. (2017). Volkswagen’s Diesel scandal costs hit 30 billion. CNN.com. http://money.cnn.com/2017/09/29/investing/volkswagen-diesel-cost-30-billion/index.html.

Schmitt, B. (2016). Brussels’ explosive dieselgate decision can bankrupt Volkswagen. Forbes.com. https://www.forbes.com/sites/bertelschmitt/2016/09/05/brussels-explosive-dieselgate-decision-can-bankrupt-volkswagen-for-starters/#55acf8b729e0.

Sirmans, G.S., Macpherson, D., Zietz, E. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1–44.

Smith, V.K. (2008). Risk perceptions, optimism, and natural hazards. Risk Analysis, 28(6), 1763–1767.CrossRef

Sprent, P. (1998). Data driven statistical methods. London: Chapman and Hall.

Tobin, G.A., & Montz, B.E. (1988). Catastrophic flooding and the response of the real estate market. Social Science Journal, 25(2), 167–177.CrossRef

Tobin, G.A., & Montz, B.E. (1994). The flood hazard and dynamics of the urban residential land market. JAWRA Journal of the American Water Resources Association, 30(4), 673–685.CrossRef

Tobin, G.A., & Newton, T.G. (1986). A theoretical framework of flood induced changes in urban land values. JAWRA Journal of the American Water Resources Association, 22(1), 67–71.CrossRef

Tobler, W.R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(sup1), 234–240.CrossRef

Tu, C.C. (2005). How does a new sports stadium affect housing values? The case of FedEx field. Land Economics, 81(3), 379–395.CrossRef

Varian, H.R. (2014). Big data: new tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.CrossRef

Volkswagen. (2017). Chattanooga - facts. Volkswagen Group of America. http://www.volkswagengroupamerica.com/facts.html.

Winke, T. (2016). The impact of aircraft noise on apartment prices: a differences-in-differences hedonic approach for Frankfurt, Germany. Journal of Economic Geography, 17(6), 1283–1300.

Zahirovic-Herbert, V., & Chatterjee, S. (2012). Historic preservation and residential property values: evidence from quantile regression. Urban Studies, 49(2), 369–382.CrossRef

Zhang, L., & Yi, Y. (2017). Quantile house price indices in Beijing. Regional Science and Urban Economics, 63, 85–96.CrossRef

Zietz, J., Zietz, E.N., Sirmans, G.S. (2008). Determinants of house prices: a quantile regression approach. Journal of Real Estate Finance and Economics, 37(4), 317–333.CrossRef

Titel: The Impact of Exogenous Shocks on House Prices: the Case of the Volkswagen Emissions Scandal
verfasst von: Heiko Kirchhain
Jan Mutl
Joachim Zietz
Publikationsdatum: 26.04.2019
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 4/2020
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-019-09700-4

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