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Assessing Portlandu27s Smart Growth: A Comprehensive Housing Supply and Location Choice Modeling Approach

机译：评估波特兰的智能增长：全面的住房供应和位置选择建模方法

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摘要

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 authoru27s knowledge, none have studied developers as location-aware decision-making agents. This study tries to fill this gap partially by assessing the impacts of Portlandu27s smart growth policies on developersu27 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 developeru27s 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 regionu27s 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.
机译：关于“智能增长”政策的影响和有效性，有大量的实证研究；但是，很少有人考虑个人决策者的观点，据作者所知，没有人研究过开发人员作为位置感知决策者。本研究试图通过使用基于开发人员的位置选择模型评估波特兰的智能增长政策对开发人员的位置选择行为的影响，来部分弥补这一差距。本文有两个目的。通过评估“智能增长”政策对单个家庭开发商的位置选择的影响，它为理解“智能增长”政策提供了微观和行为基础。作为位于俄勒冈州和华盛顿州交界处的两州大都市区，波特兰地区提供了独特的环境，使我的研究能够检查在具有不同土地使用政策体系的两个州，房屋开发商对智能增长政策的反应是否不同。本文还旨在建立一个基于开发人员的土地开发预测模型，该模型可作为波特兰地区长期土地使用和运输规划的情景分析工具。除了上面提到的开发商位置选择模型外，这种基于开发商的全面土地开发模型的组成部分还包括一个预测该地区年度新住房供应的时间序列回归模型，以及一个在预测年度内综合住房项目的模型。该研究表明，波特兰大都市地区的房屋开发商对该地区已实施的大多数“智能增长”政策敏感，但他们对俄勒冈州和华盛顿州边界的反应不同。单户住宅（SFH）和多户住宅（MFH）开发人员对位置属性显示不同的偏好。开发人员将选择放置项目的位置的最重要的预测因素是先前项目的位置。在控制完上述所有其他因素之后，仍然强烈希望在波特兰市区的俄勒冈州和华盛顿州在UGB之外开发SFH装置。已经开发了潜在类模型，以分别检测SFH和MFH市场中房屋开发商之间的口味差异。估算结果表明，在开发商和房屋项目中，根据其位置选择中的场地属性，其口味明显不同。在细分模型中的其他变量相同的情况下，项目大小比开发人员的大小更适合数据，这表明开发人员在不同项目之间存在品味差异。由承包商业主开发的大型SFH项目更有可能位于UGB内，其所在地往往具有更高的住宅密度，住房多样性，交通便利性，道路密度和土地价格。对于UGB中的大多数MFH项目，估计结果表明，大型MFH项目更喜欢居住密度较高，住房多样化，混合使用，道路密度，土地价格，平均家庭收入以及中青年家庭比例较高的地区。新的三步式住房供应和位置选择预测模型似乎能够抓住波特​​兰地区住房市场和土地开发的基本趋势。测试了三种不同的总住房供应预测模型，条件时间序列回归模型，无条件时间序列回归模型和自回归综合移动平均（ARIMA）模型，并讨论了它们的优缺点。 SFH和MFH项目综合模型都可以很好地模拟预测年份的住房项目。开发了三个位置选择模型，以将综合住房项目分配到空间中。这三个模型分别具有以下特征：（1）假设市场开发项目的市场同质性和雾化性； （2）确定性市场细分和按规模划分的项目综合； （3）使用潜在类别方法按规模对项目进行概率市场细分和综合。对预测结果的检验表明，所有三个模型都可以成功地捕捉到该地区住房发展的基本空间格局。但是，MFH发展的空间分布更为块状，并且更加不可预测。尽管模型2和3更复杂，并且从理论角度讲更有意义，但由于一些实际问题，它们没有比模型1更好地返回预测结果。当解决这些实际问题（至少在将来的研究中）时，预计模型2和3会表现更好。

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    Dong Hongwei;

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  • 年度 2010
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