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Abstract
The redevelopment of land containing single-family detached dwellings into small attached or multiple-family structures is a common method of densification in existing urban areas. The potential for redevelopment of any existing home is an important consideration for housing market participants, real estate developers and public officials. Using a longitudinal data-set from the City of Seattle, this study quantifies the impact that a number of factors – policy, physical, neighbourhood and market – have on the likelihood of this form of land use conversion. Derived with a duration model, these findings suggest that the size of the existing home, the adjacent land uses and, most importantly, factors affecting the size of the potential redevelopment have the largest impact on the probability of redevelopment.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. The following filtering criteria were applied to the original data-set of 8715 properties in order to eliminate observations with uncommon data or characteristics not representative of a typical single-family home at risk for redevelopment. Removed were observations with: (1) lot size less than 2000 or more than 20,000 square feet; (2) structural size less than 400 or greater than 8000 square feet; (3) a structure of more than four stories; and (4) a 2003 assessed value per square foot of land greater than $100 or per square foot of improvement greater than $300. In sum, 348 total observations were removed from the original data-set of 8715 (3.99%).
2. Of these redevelopments, 98% involved townhome or rowhouse construction. The remaining 2% are composed of small apartments and condominiums.
3. Rates are calculated as the redevelopment rate of the nearest 100 observations to each grid point on the map.
5. Condition and quality based on determinations by that King County Assessor.
6. The resulting calculation is scaled by a factor of 100 for interpretation purposes.
7. The models used in this research have been estimated in the R software package (R Core Team, Citation2013) using the coxph() function from the survival package (Therneau, Citation2013). The data is singly, right-censored (Type I).
8. Full model results are available in the online appendices, hosted at www.andykrause.com/publications.
9. The hedonic price literature has shown that non-linear (Clapp & Salavei, Citation2010) or vintage effects (Bitter, Citation2014; Coulson & McMillen, Citation2008) often exist when modelling the relationship between home age and home prices. Tests of non-linear effects on the YEARBUILT variable added no additional explanatory power to the model. Additionally, tests of vintage effects suggest that grouping homes by vintage decreases the explanatory power of the model and does not impact other coefficient estimates. As a result, neither non-linear nor vintage effects were considered in the final model specifications. In short, non-linear and vintage effects found to impact home prices may be less pronounced in determining redevelopment probability.
10. Sensitivity tests using alternate neighbourhood definitions of 200 and 800 m produced very similar parameter estimates for both the neighbourhood/location variables and other co-variates.
11. In this context, localised is defined as the nearest 500 home sales within the past three years.
12. From Therneau (Citation2013, p. 79): ‘Concordance is defined as Pr(agreement) for any two randomly chosen observations, where in this case agreement means that the observation with the shorter survival time of the two also has the larger risk score. The predictor (or risk score) will often be the result of a Cox model or other regression. For continuous covariates concordance is equivalent to Kendall’s tau, and for logistic regression it is equivalent to the area under the ROC curve. A value of 1 signifies perfect agreement, .6–.7 is a common result for survival data, .5 is an agreement that is no better than chance and .3–.4 is the performance of some stock market analysts’.
13. Note that AIC and logLik values cannot be compared between Models 1/2 and Models 3/4 due to differences in the underlying data.
14. The ‘binding-ness’ of the zoning regulations in these zones is evidenced by the threshold effect on redevelopment probability observed when moving from LR1 to LR2 or LR3 zones.