First Advisor

John Gliebe

Date of Publication


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Urban Studies


Urban Studies and Planning




Sustainable development -- Oregon -- Portland, Homesites -- Oregon -- Portland, Urban Land use -- Oregon -- Portland



Physical Description

1 online resource (x, 157 p.) : ill. (some col.), maps (some col.)


There are extensive empirical studies on the impacts and effectiveness of Smart Growth policies; however, very few of them consider the perspective of individual decision makers and, to this author's knowledge, none have studied developers as location-aware decision-making agents. This study tries to fill this gap partially by assessing the impacts of Portland's smart growth policies on developers' location choice behavior with developer-based location choice models. The dissertation has two purposes. By assessing the impacts of Smart Growth policies on individual home developer's location choice, it provides a micro- and behavioral foundation for the understanding of Smart Growth policies. As a bi-state metropolitan area located on the border between Oregon and Washington, the Portland region provides a unique environment that allows my research to examine whether home developers react to Smart Growth policies differently in the two states with different land use policy systems. The dissertation also aims to create a developer-based land development forecast model, which can be used as a scenario analysis tool for the Portland region's long-term land use and transportation planning. Besides the developer location choice model mentioned above, the components of this comprehensive developer-based land development model also include a time series regression model that predicts annual new housing supply in the region and a model that synthesizes housing projects in a forecast year. The study shows that home developers in the Portland metropolitan area are sensitive to most Smart Growth policies that have been implemented in the region, but they react to them differently across the border between Oregon and Washington. Single-family home (SFH) and multi-family home (MFH) developers show different preferences for location attributes. The most significant predictors of where a developer will choose to locate a project are the locations of previous projects. After controlling for all of the other factors discussed above, there remains a strong preference for developing SFH units outside of the UGB in both Oregon and Washington sides of the Portland metropolitan area. Latent class models have been developed to detect taste variations among home developers in the SFH and MFH markets separately. Estimation results show clear taste variations across developers and housing projects with respect to site attributes in their location choice. With other variables in the segmentation model being the same, project size provides a better fit to the data than developer size, indicating that developers have taste variations among their different projects. Large size SFH projects developed by contractor-owners are more likely to be within the UGB and their locations tend to have higher residential density, housing diversity, transportation accessibility, road density, and land price. With most MFH projects within the UGB, estimation results show that large size MFH projects prefer the locations with higher residential density, housing diversity, mixed use, road density, land price, average household income, and proportion of young and middle age households. The three-step new housing supply and location choice forecast model seem to be able to capture the basic trend of housing market and land development in the Portland region. Three different aggregate housing supply forecast models, an conditional time series regressive model, a unconditional time series regression model, and an auto-regression integrated moving average (ARIMA) model were tested and their advantages and disadvantages were discussed. Both the SFH and MFH project synthesis models can simulate housing projects well for a forecast year. Three location choice models were developed to allocate synthesized housing projects into space. The three models are characterized separately as: (1) assumed market homogeneity and atomization of development projects; (2) deterministic market segmentation and synthesis of projects by size; and (3) probabilistic market segmentation and synthesis of projects by size, using a latent class approach. Examination of forecast results shows that all three models can successfully capture the basic spatial pattern of housing development in the region; however, the spatial distribution of MFH development is lumpier and more unpredictable. While Models 2 and 3 are more sophisticated and make more sense from a theoretical perspective, they do not return better forecast results than Model 1 due to some practical issues. Models 2 and 3 would be expected to perform better when those practical issues are solved, at least partially, in future research.


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Nohad A. Toulan School of Urban Studies and Planning

Persistent Identifier