Demand for new cities: Property value capitalization of municipal incorporation

Patrick, Carlianne; Mothorpe, Christopher

doi:10.1016/j.regsciurbeco.2017.09.002

Cited by 8 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their similar analysis, Patrick and Mothorpe (2017) found the most significant predictors of municipal incorporation to be average neighborhood sales price, the neighborhood to county price ratio and neighborhood resident age group shares. They did not find average neighborhood housing characteristics, in general, to be significant in the prediction of incorporation, nor did resident income shares appear to be important.…”

Section: "At Risk" Estimation and Data Preprocessingmentioning

confidence: 93%

“…Additionally, it is possible that property tax limitations and other policies unique to California may affect the capitalization of any benefits into property values. For example, in Georgia, where Patrick and Mothorpe (2017) examined the effect of municipal incorporations on residential property values, property is assessed at the county level at a rate of 40% of the fair market value and properties are reassessed regularly. In California, property taxes are limited to 1% of the acquisition value of the property and increases cannot exceed 2% per year.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Another stated reason for incorporation in the communities we examine in Riverside County, California, was the goal of "retain[ing] local revenues for benefit in the community." This reason appeared to be at least as important as preserving the community's shared identity and heritage, and may have reflected a desire by wealthier residents to avoid redistribution via the subsidization of public goods provision for lower income residents (Patrick and Mothorpe, 2017). Kenny and Reinke's (2011) research building on a city-county consolidation model (Filer and Kenny, 1980) also supports this view by illustrating that the richest neighborhoods have the strongest incentives to incorporate in order to avoid redistribution through taxation and local public goods provision.…”

Section: X Xmentioning

confidence: 99%

“…The municipalities examined by Patrick and Mothorpe (2017) were also observed to have similarly taken control of the provision of certain public services, while also contracting with the county for others. However, unlike in California, the Georgia municipalities were also observed to have increased property taxes which is expressly prohibited in California.…”

Section: Public Service Provision Post-incorporationmentioning

confidence: 99%

“…Further, there exists the ever-present possibility that the observable housing characteristics do not adequately control for unobservables or that counterfactual trends are invalid (Ferraro and Miranda, 2014a). As noted in Patrick and Mothorpe (2017), the difference-in-difference (DID) hedonic methodology addresses some, though not all, of these econometric challenges. While hedonic DID allows for the identification of the capitalization effect, by comparing the differences in housing sales prices before and after municipal incorporation between treated and untreated groups, it does not guarantee a causal estimate.…”

Section: Econometric Approachmentioning

confidence: 99%

See 4 more Smart Citations

Three Essays in Applied Econometrics

Hodges¹

View full text Add to dashboard Cite

Three Essays in Applied Econometrics Collin Dean Hodges The body of work presented here consists of a collection of research projects developed during my time as a graduate student at West Virginia University. As will soon become apparent, this collection of research topics is quite eclectic. This is, in part, due to the to the nature of the research First and foremost, thank you to the members of my dissertation committee, Heather Stephens, Joshua Hall, Daniel Grossman and Xiaoli Etienne for their research feedback and guidance navigating the academic job market. The pay was zero and the work non-zero, but for your willingness to serve as members of my committee, I will remain always grateful. Heather, thank you for your assistance in obtaining what, at times, seemed to be impossible to obtain datasets and for always being generous with your time. To Josh, thank you for your frequent guidance, the numerous opportunities to gain valuable academic experiences, and your support throughout my graduate career. There is no question that I would not be here today without your support. To Dan, thank you for the part you played in introducing me to the field of health economics. I consider myself very fortunate to have been a part of the initial offering of these fascinating field courses at West Virginia University. To Xiaoli, thank you for your constructive comments and feedback, as well as for setting the bar high with regard to article reading. I have not quite reached your level of one article a day. I would also like to thank my undergraduate professors, Pete Calcagno, Malavika Nair and Doug Walker, for their initial suggestion that I look further into this whole "graduate school" thing. Your amazing classes at the College of Charleston are why I am where I am today. Pete, thank you for your friendship, guidance, the great work you continue to do with the Center for Public Choice and Market Process and the numerous opportunities you provide for undergraduate students to engage with economics ideas outside of the classroom. To Mal, thank you for being the first to encourage me to pursue a graduate degree. It was certainly something that, at the time, seemed well beyond my reach. To Doug, thank you for your friendship and advice over the years. I look forward to catching up with you and Pete in Charleston very soon. I also must, of course, thank my occasional office-mates and friends, Elham Erfanian and Caleb Stair. Elham, I am exceedingly grateful that you possess a healthy and, apparently, inexhaustible sense of humor. Thank you for sharing your eternal optimism with me and for always having a

show abstract

Section: "At Risk" Estimation and Data Preprocessingmentioning

confidence: 93%

Section: Background and Motivationmentioning

confidence: 99%