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Determinants of historic and cultural landmark designation: why we preserve what we preserve

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Abstract

There is much interest among cultural economists in assessing the effects of heritage preservation policies. There has been less interest in modeling the policy choices made in historic and cultural landmark preservation. This article builds an economic model of a landmark designation that highlights the tensions between the interests of owners of cultural amenities and the interests of the neighboring community. We perform empirical tests by estimating a discrete choice model for landmark preservation using data from Chicago, combining the Chicago Historical Resources Survey of over 17,000 historic structures with property sales, Census, and other geographic data. The data allow us to explain why some properties were designated landmarks (or landmark districts) and others were not. The results identify the influence of property characteristics, local socio-economic factors, and measures of historic and cultural quality. The results emphasize the political economy of implementing preservation policies.

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Notes

  1. Although preservation of buildings or other structures is the primary focus of this article, the model can be readily extended to preservation of other heritage resources, such as artwork or cultural landscapes. The core idea—that a regulator balances interests of the owners (who enjoy options to transform or dispose of the resource) and some external constituency who receives a positive externality from that resource—holds across a variety of cultural applications.

  2. These exogenous factors will include characteristics of individual properties, owners, or neighborhoods and will be discussed below. The direct and indirect preferences will vary depending on which exogenous factor is considered.

  3. A sufficient condition for this condition to hold are that administrative costs increase more-than-linearly, that the added benefit to neighbors is decreasing in restrictiveness, and that the costs imposed on property owners by restrictions increases about linearly.

  4. For policy instrument c = n, or no regulation, the probability of being unregulated is set to be equal to Eq. 6 with the numerator replaced with 1.

  5. Due to data limitations, we assume that CHRS data were collected by 1990. For CHRS properties surveyed after 1990, it is possible an endogeneity or sample selection bias might occur. The dependent variable (post-1990 designation status) might influence the independent variables from the CHRS dataset or even the likelihood of inclusion in the CHRS. If designated properties are more likely to be included, then inferences drawn from estimates using this sample may not be valid for the broader population of structures. For instance, oversampling designated-but-low-quality buildings might artificially lower the estimated effect of quality of designation. If designation status affects how surveyors recorded property information—or affected the building attributes directly—then classic endogeneity occurs. For example, designated buildings might receive higher quality scores than they would have without the prestige of designation, biasing upwards the estimated effect of quality on designation.

  6. The Great Chicago Fire of 1871 devastated much of the city core at the time, forcing a virtual rebuilding of this portion of the city. This puts an upper limit on building age in that area. As the begun variable is missing for many observations, begun is coded with a 0 for all missing observations and a dummy variable (nobegun) is included to capture the mean effect of those buildings with missing begin values. See Alberini and Longo (2006) for an application of this approach.

  7. Strictly speaking, the model predicts whether structures are not designated, designated in a district, or individually designated at some point after 1990. As a structure that was designated prior to 1990 cannot, in general, be re-designated during the 1990s, we drop these structures form the data set in all the regressions reported here.

  8. Some of these coefficients are insignificant for one of the designation types and not statistically different from one another at conventional levels. In those cases, we report the sign of the significant coefficient.

  9. The connection between income and education as determinants of demand for heritage preservation, and cultural goods in general, is discussed in greater detail elsewhere (e.g., Bourdieu 1984). This is another instance of education being a better predictor of cultural demand than income (e.g., Heilbrun and Gray 2001; Whitehead and Finney 2003; Alberini and Longo 2006).

  10. If CountCHRS distinguished between red and orange CHRS properties and other CHRS properties, the unrestricted model above would show (not reported here) that it is count of red and orange properties nearby that drives this effect.

  11. Interestingly, the percentage of old housing lost during the 1990s is positive and significant when included in a similar regression. Because this variable is endogenous, we do not report any such regressions here. Its significance could be interpreted cynically (that preservation policies lead owners to demolish houses). Another interpretation is that high rates of demolition increase the demand for preservation by neighbors, leading administrators to preserve areas “threatened” by development. Much hinges on the timing of the preservation and the demolition, which we cannot address here.

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Correspondence to Douglas S. Noonan.

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This document contains demographic data from Geolytics, Inc East Brunswick, NJ.

Appendix

Appendix

See Appendix Tables 5 and 6.

Table 5 Variable descriptions
Table 6 Descriptive statistics

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Noonan, D.S., Krupka, D.J. Determinants of historic and cultural landmark designation: why we preserve what we preserve. J Cult Econ 34, 1–26 (2010). https://doi.org/10.1007/s10824-009-9110-6

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