Deep learning for house prices
Abstract/Contents
- Abstract
- This dissertation develops, estimates, and analyzes a deep learning model for home prices using an unprecedented dataset containing over 150 million sale transactions between 1970 and 2019 in the United States. We contrast our model's superior performance with methods commonly used in the literature, and uncover previously unstudied pricing factors. The last sale amount figures as the most influential variable, and we identify localized statistics, house characteristics, mortgage rates, and macroeconomic conditions to be among the most salient features. We also contribute to the literature in demonstrating our model's capacity to capture non-linear effects, exposing, for example, the model's increased sensitivity to local statistics in times of high and low economic prosperity. In the second part, we use the trained network to scrutinize the efficiency of housing markets —which assumes all information is contained in home prices— in a set of novel approaches. We challenge the efficiency notion by finding that model-induced price trends can be predictive of future returns, and devise a trading strategy that exploits such effects to produce unparalleled profits. In an out-of-sample simulation of house trading from 1995 to 2019, our deep learning-induced strategy produces the highest average returns (at an annualized rate of over 25%) when compared to benchmark methods, and we find that short term resales yield the highest performance. Finally, we find evidence that suggests the market is not efficient (there are large gains to be made using only past information), and expose the areas with the highest price exploitability, such as California, Washington State, Wyoming, Colorado, New Mexico, Massachusetts, New York, Connecticut, and New Jersey.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ramos, Bernardo |
|---|---|
| Degree supervisor | Giesecke, Kay |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Lai, T. L |
| Thesis advisor | Pelger, Markus |
| Degree committee member | Lai, T. L |
| Degree committee member | Pelger, Markus |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Bernardo Ramos. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/wx673rx3650 |
Access conditions
- Copyright
- © 2021 by Bernardo Ramos
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