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Do residential reconversions affect residential property values? An investigation based on Québec city (Canada)

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Abstract

Residential reconversion can foster density and change the structure of neighborhoods. It also upsets current residents when it affects or obstructs their original panorama. Many nearby homeowners argue that new construction of visually imposing residential buildings negatively affects the value of their house. The aim of this paper is to test such a presumption by investigating whether single-family houses’ prices are affected (or not) by being close to reconversions. The analysis is based on an exhaustive data set of residential reconversions that were recorded between 2006 and 2016 and is combined with a database of single-family transactions sold in Quebec City between 2004 and 2017. The results suggest that residential reconversions lead to a mean net price premium of about 2.48%. This effect, however, varies according to the type of residential reconversion as well as to the density of the reconversions. Results show no negative significant effects, which suggests that the reduction in house prices expected by residents, i.e., sellers, is largely compensated for by buyers’ attraction, as expressed by market equilibrium.

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Notes

  1. KDR has already been studied for Australian cities (Pinnegar et al., 2015a; Pinnegar et al., 2015b; Wiesel et al., 2013), as well as American cities, such as Chicago (Charles, 2013, 2014; Dye & McMillen, 2007; Helms, 2003; Weber et al., 2006), New York (Been et al., 2009; Hirshey, 2008) and Miami (Munneke & Womack, 2015), and Canadian Cities such as Vancouver (Rosenthal & Helsley, 1994).

  2. The assumption that the implicit prices of the characteristics is constant can easily be relaxed if needed. However, this assumption is also implicitly made in most of the HPM empirical applications. It is also useful when very few information about the individual characteristics is available, which is the case here.

  3. The analysis can also be done on a limited sample size of the repeated sales, i.e. those who record a change between the moment of the sale and the resale.

  4. The total sample size is noted nT in the RS approach, while the sample size is NT in HPM, where nT = Σtnt, where nt is the total number of repeated sales in time t, and NT = ΣtNt where Nt is the total number of (single) transactions in time t. In the end nT < NT, and the question is to know if the characteristics of the subsample in nT is similar (or statistically equal) to the ones in NT (for all characteristics).

  5. The approach also helps to solve the problem related to the fact that few house characteristics are detailed and available. Only the age, lot size, living area, and the number of floors is available.

  6. This situation corresponds to the 10-centile rank of the possession time within all repeated transactions.

  7. The significance of the effect for the observations that has a viewshed is obtained by testing H0: β100m + βview = 0, and not only on the significance of the parameter βview. The marginal effect is calculated, for the 100-m area, by exp(β100m)—1, and by exp(β100m + βview)—1 for the viewshed area.

  8. The marginal effect is calculated, for the 100-m zone, by exp(β100m + βtype;100 m)—1, and for the viewshed by exp{(β100m + βview) + (βtype;100 m + βtype; view)}—1.

  9. The marginal effect for the 100-m area is given by exp{β100m + βintensity;100 m × (log(# reconversionresale + 1)—log(# reconversionsale + 1))}—1, where # reconversionresale is the total number of reconversion within a 250-m zone at the resale, and # reconversionsale is the total number at the sale. The marginal effect for the viewshed area is given by exp{(β100m + βview) + (βintensity;100 m + βintensity;view) × (log(# reconversionresale + 1)—log(# reconversionsale + 1))}—1.

  10. With the marginal effect within the 100-m area by type given by exp{(β100m + βtype;100 m) + (βintensity;100 m + βintensity;type;100 m) × (log(# reconversionresale + 1)—log(# reconversionsale + 1))}—1, while the impact for viewshed area by type is given by exp{[(β100m + βview) + (βtype;100 m + βtype;view)] + [(βintensity;100 m + βintensity;view) + (βintensity;type;100 m + βintensity;type;view)] × (log(# reconversionresale + 1)—log(# reconversionsale + 1))}—1.

  11. Also called the Yes in my backyard (YIMBY).

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Acknowledgements

This research has been funded by the Canadian Social Sciences and Humanities Research Council (SSHRC) by the administrative organization of Québec City

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  1. Université Laval, 2325 Rue Des Bibliothèques, Pavillon Félix-Antoine-Savard, Quebec, QC, G1V 0A6, Canada

    Jean Dubé & François Des Rosiers

  2. INRS-UCS, Montreal, QC, Canada

    Maha AbdelHalim

  3. UQAR, Rimouski, QC, Canada

    Nicolas Devaux

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  1. Jean Dubé
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Dubé, J., AbdelHalim, M., Des Rosiers, F. et al. Do residential reconversions affect residential property values? An investigation based on Québec city (Canada). J Hous and the Built Environ 38, 2373–2397 (2023). https://doi.org/10.1007/s10901-023-10041-1

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