Abstract
Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model uncertainties by integrating the outputs of basic models. Basic models must have a good performance for their combinations to have acceptable results. This study employs four statistical and machine learning models as basic models: Random Forest (RF), Ordinary Least Squares (OLS), Weighted K-Nearest Neighbour (WKNN), and Support Vector Regression (SVR) to estimate the price of residential properties. The results show that the integrated output is more accurate for the quadruple combination mode than for any of the binary and triple combinations of the basic models. Comparing the Stacking technique with the Voting technique, it is shown that the Mean Absolute Percentage Error (MAPE) reduces from 10.18% to 9.81%. Hence we conclude that our method performs better than the Voting technique.
Original language | English |
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Title of host publication | XXIV ISPRS Congress “Imaging today, foreseeing tomorrow” |
Subtitle of host publication | Commission IV |
Editors | S. Zlatanova, G. Sithole, J. Barton |
Place of Publication | Nice |
Publisher | Copernicus |
Pages | 49-55 |
Number of pages | 7 |
Volume | 5 |
Edition | 4 |
DOIs | |
Publication status | Published - 17 May 2022 |
Event | 24th ISPRS Congress 2022 - Nice, France Duration: 6 Jun 2022 → 11 Jun 2022 Conference number: 24 |
Publication series
Name | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Publisher | Copernicus |
ISSN (Print) | 2194-9042 |
Conference
Conference | 24th ISPRS Congress 2022 |
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Country/Territory | France |
City | Nice |
Period | 6/06/22 → 11/06/22 |
Keywords
- Ordinary Least Squares
- Random Forest
- Stacking. Residential Property Valuation
- Support Vector Regression
- Uncertainty Modelling
- Weighted K-Nearest Neighbours