arXiv 14 Oct 2021 · Econometrics · 8 citations (OpenAlex)
arXiv:2110.07151 · PDF · DOI · OpenAlex · Extracted main text
In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Breiman, L (2001) Random forests | 0.737 | 3 | 2 | 100% |
| 2 | Hong, J., Choi, H., and Kim, W.-s (2020) A house price valuation based on the random forest approach: the mass appraisal of residential property in south korea | 0.737 | 3 | 2 | 100% |
| 3 | Park, B. and Bae, J. K (2015) Using machine learning algorithms for housing price prediction: The case of fairfax county, virginia housing data | 0.737 | 3 | 2 | 100% |
| 4 | Selim, S (2011) Determinants of house prices in turkey: A hedonic regression model | 0.737 | 3 | 2 | 100% |
| 5 | Ali, G., Bashir, M. K., Ali, H., et al (2015) Housing valuation of different towns using the hedonic model: A case of faisalabad city, pakistan | 0.644 | 2 | 2 | 100% |
| 6 | Curry, B., Morgan, P., and Silver, M (2002) Neural networks and non-linear statistical methods: an application to the modelling of price–quality relationships | 0.644 | 2 | 2 | 100% |
| 7 | Islam, K. S. and Asami, Y (2009) Housing market segmentation: A review | 0.644 | 2 | 2 | 100% |
| 8 | Kauko, T., Hooimeijer, P., and Hakfoort, J (2002) Capturing housing market segmentation: An alternative approach based on neural network modelling | 0.644 | 2 | 2 | 100% |
| 9 | Lenk, M. M., Worzala, E. M., and Silva, A (1997) High-tech valuation: should artificial neural networks bypass the human valuer? | 0.644 | 2 | 2 | 100% |
| 10 | Levantesi, S. and Piscopo, G (2020) The importance of economic variables on london real estate market: A random forest approach | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Real Estate Property Valuation using Self-Supervised Vision Transformers | 0.405 | 1 | 1 |