Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


JeffBlahaThesisFinal.pdf (37.47 MB)

ETD Abstract Container

Abstract Header

Variable Selection Methods for Residential Real Estate Markets: An Exploration of Random Forest Trees in Spatial Economics

Blaha, Jeffrey
http://orcid.org/0000-0001-9787-1774
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1503330225924692

Abstract Details

2017, Master of Arts, University of Toledo, Economics.
Little is known about the interaction of spatial dependent models and random decision forests. Most previous research has not implemented modern machine learning techinques with economics let alone spatial econometrics. In this paper we apply random forest analysis with a spatial dependent component to hedonic pricing models. This paper sought to improve parameter identification, prediction performance, and bridge the gap between spatial economics and machine learning. The data provided details 45,381 residential real estate sales in Lucas County, Ohio between 2001-2016. Evaluation by log-linear and spatial log-linear models shows that random forests can make comparatively accurate model predictions using less indicators than models selected by conventional methods. While the spatially dependent random forest models did not produce the lowest root mean square error compared to the spatially dependent models, reducing the number of parameters by 35\% only marginally increased error compared to other models. The results have implications for improving understanding of components used real estate appraisal as well as construction or investment.
Oleg Smirnov (Committee Chair)
Aliaksandr Amialchuk (Committee Member)
Kristen Keith (Committee Member)
84 p.
Economics
Spatial; Econometrics; Random Forest; Machine Learning; Economics; Real Estate; Toledo

Recommended Citations

Citations

  • Blaha, J. (2017). Variable Selection Methods for Residential Real Estate Markets: An Exploration of Random Forest Trees in Spatial Economics [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1503330225924692

    APA Style (7th edition)

  • Blaha, Jeffrey. Variable Selection Methods for Residential Real Estate Markets: An Exploration of Random Forest Trees in Spatial Economics. 2017. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1503330225924692.

    MLA Style (8th edition)

  • Blaha, Jeffrey. "Variable Selection Methods for Residential Real Estate Markets: An Exploration of Random Forest Trees in Spatial Economics." Master's thesis, University of Toledo, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1503330225924692

    Chicago Manual of Style (17th edition)

toledo1503330225924692
129
© 2017, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.