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An Erratum to this article was published on 23 November 2011

Abstract

The creation of historic districts has become a common way to preserve historic buildings and neighborhoods. Advocates of historic districts assume that such districts augment, or at least, protect property values for homes within these districts. The existing economic literature supports this conclusion, but most studies seem to fall victim to an endogeneity bias since higher value homes are, all else equal, more likely to be included in districts. This study uses repeat-sales fixed effects (difference-in-differences) analysis to look at homes before and after the creation of districts in the Boston-Cambridge-Quincy MSA between 2000 and 2007, and thus control for this endogeneity bias. Secondarily, we re-examine the effects of a Massachusetts preservation policy, the Community Preservation Act (CPA) which, in part, supports historic preservation. We find evidence that the creation of a local historic district, on average, reduces home prices for homes in that district between 11.6 and 15.5%. This indicates that any restrictions implied by the creation of a district outweigh any benefits to homeowners within the district. If, instead, census block fixed effects are employed, the analysis shows a statistically insignificant impact, the sign and magnitude of which depends on the specification. Taken together with the repeat sales result, this confirms our intuition about the importance of controlling for omitted variables and endogeneity biases. Finally, we find evidence that the CPA also lowers property values, by less than 1%, and that being in a Historic District magnifies the negative effect of the CPA.

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Notes

  1. National Trust for Historic Preservation, http://www.nps.gov/hps/workingonthepast/does_doesnot.htm.

  2. Historic preservation can be characterized this way because it exhibits both characteristics of a public good: it is both non-rivalrous and non-excludable. To the extent that historic preservation has non-use values, that is, people value knowing that historical properties exist, even if they do not plan to visit or otherwise exploit them, these traits are clear. Even in considering use values, however, in most ways, my enjoyment of a historic neighborhood, by visiting it, does not prevent anyone else from similarly enjoying it (non-rival) and we similarly cannot exclude people from enjoying a historic district once it is preserved.

  3. What we refer to as subsidies are, in fact, any policy that provides a monetary benefit to homeowners whose homes are designated as part of a historic district. These can take the form of tax breaks, grants, or explicit subsidies to enable the protection of designated properties. See (Noonan, forthcoming) for more detail on these policies.

  4. See the website of the National Trust for Historical Preservation for descriptions and data regarding teardowns in the United States. http://www.preservationnation.org/issues/teardowns/.

  5. If the effect does vary with the age of the home, this would be made evident through the inclusion of a term interacting designation with age.

  6. For a more thorough review of this literature, see (Noonan, forthcoming). For a survey and analysis of hedonic analyses more generally see Sirmans et al. (2005) and Sirmans et al. (2006).

  7. Massachusetts General Laws: Chapter 40C.

  8. “Summary of an Act to Sustain Community Preservation, SB 90”, available at http://www.communitypreservation.org/CPALegislation.cfm.

  9. A database of CPA projects can be accessed at http://www.communitypreservation.org/CPAProjectsSearchStart.cfm.

  10. It is not sufficient to include the District dummy variable and the linear distance since this would still force a monotonic relationship between distance and price because the distance for those inside of a district is zero. Such a monotonic relationship is unrealistic.

  11. As discussed above, if external effects exist, we would expect homes closer to districts to be more impacted than those further away. We include such large dummies in the hopes of identifying a distance at which districts cease to have external effects, if they exist.

  12. Unfortunately, detailed information on the approval process for the districts in our sample is not readily available, and there is likely considerable heterogeneity in this process from district to district, so we are unable to systematically control for any anticipatory bias. There are fewer than ten transactions in our dataset, however, that take place within a new district fewer than 12 months before that district was created, so any bias is presumably quite small.

  13. We employ fixed effects because it is the most appropriate for our context. Instrumental variables requires us to be able to identify an observable variable that is correlated with historic designation but not property values. We were unable to identify such a variable. A regression discontinuity approach relies on using homes on either side of an exogenous border. This approach was considered, but evidence suggests that historic district boundaries are not exogenous, otherwise we would not have to worry about this at all. We were unable to identify another border, geographic or otherwise, which could provide this exogeneity.

  14. There is one implicit assumption that we have to make for this fixed effects analysis to be valid. This is that there are no time varying unobservables that are correlated with the creation of historic districts and with property values at the very local level. This type of omitted variable could have similar effects as traditional omitted variables bias. Our normalization of the prices reduces concerns about any large-scale omitted variables of this type, but, while we have no reason to believe that any smaller scale effects of this type are at play, we cannot completely rule them out.

  15. For homes that sell more than once, we only observe property characteristics at the time of the most recent sale. To the extent that there are renovations to the home, we are unable to observe this. We are also limited in terms of which characteristics can be included, again, because of what is available in the dataset.

  16. Since our fixed effects analysis depends on changes in a parcel’s status as regards historic preservation over time, it is important to know how much variation we actually observe in these variables. In our dataset, there are 87 observations for 39 parcels in seven different districts whose InDistrict status changes during the sample period.

  17. We also tried using fixed effects and clustering at the census block-group level. Results were broadly consistent with those presented here, and are available from the authors.

  18. Importantly, the InDistrict and Age/District interaction terms are extremely highly collinear which helps explain why the inclusion of the interaction term makes the InDistrict coefficient become insignificant. This also helps to justify the interpretation above that the results are indicative of a positive internal effect of historic districts.

  19. We did experiment with non-linear specifications of many of these variables, especially the distance variables. Because these effects are not the focus of this paper, these results are not reported, but are available from the authors.

  20. By restricting the sample in this way to homes that sell more than once, we risk the introduction of a sample bias. However, an examination of summary statistics suggests no substantial differences in observed characteristics between those homes that do and do not sell more than once during the sample period. Homes selling more than once tend to be somewhat smaller and have a correspondingly lower sales price, but these differences are not large. More importantly, the fixed-effects analysis implicitly controls for anything, observed or unobserved, that makes these homes different, and so any differences should not be reflected in the estimated effect of historic districting. Due to space constraints, summary statistics broken out by repeat/non-repeat are not included in this manuscript, but are available from the authors.

  21. Notice that the adjusted R 2s in these models are very small. This is because, at this scale, we are only using within parcel variation, so that most of the overall variation is explained by the unestimated parcel level fixed effects parameters. The R 2 statistic does not include that variation explained by these parameters, α i in Eq. 2. See Cameron and Trivedi (2010), Chapter 8.

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Acknowledgements

Thanks are due to Todd Easton, Douglas S. Noonan, Stephen Sauer, and seminar participants at Binghamton University and the 2011 Eastern Economics Association Meetings for comments and suggestions which have improved this manuscript. Funding from the Clarkson University School of Business and the Fredric C. Menz Endowment Fund made this work possible. All errors are, of course, our own.

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Correspondence to Martin D. Heintzelman.

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Heintzelman, M.D., Altieri, J.A. Historic Preservation: Preserving Value?. J Real Estate Finan Econ 46, 543–563 (2013). https://doi.org/10.1007/s11146-011-9338-8

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