| Title |
Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic / |
| Authors |
Grybauskas, Andrius ; Pilinkienė, Vaida ; Stundžienė, Alina |
| DOI |
10.1186/s40537-021-00476-0 |
| Full Text |
|
| Is Part of |
Journal of Big Data.. London : Springer Nature. 2021, vol. 8, iss. 1, art. no. 105, p. 1-20.. ISSN 2196-1115 |
| Keywords [eng] |
machine learning ; TOM ; real estate ; apartments ; Big Data ; pandemics |
| Abstract [eng] |
As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to infuence a price revision during the pandemic. The fndings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional efect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the frst wave of the COVID-19 pandemic. Afterwards, 15 diferent machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The fndings in this study coincide with the previous literature results, afrming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as frst believed. Out of the 15 diferent models tested, extreme gradient boosting was the most accurate, although the diference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour. |
| Published |
London : Springer Nature |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
