Abstract
Infonavit, the largest provider of mortgages in Mexico, assists working families to obtain low-interest rate housing solutions. An increasingly prevalent problem is home abandonment: when a homeowner decides to leave their property and forego their investment. A major causal factor of this outcome is a mismatch between the homeowner's needs, in terms of access to services and employment, and the location characteristics of the home. This paper describes our collaboration with Infonavit to reduce home abandonment at two levels: develop policy recommendations for targeted improvements in location characteristics, and develop a decision-support tool to assist the homeowner in the home location decision. Using 20 years of mortgage history data combined with surveys, census, and location information, we develop a model to predict the probability of home abandonment based on both individual and location characteristics. The model is used to develop a tool that provides Infonavit the ability to give advice to Mexican workers when they apply for a loan, evaluate and improve the locations of new housing developments, and provide data-driven recommendations to the federal government to influence local development initiatives and infrastructure investments. The result is improving economic out-comes for the citizens of Mexico by preemptively identifying at-risk home mortgages, thereby allowing them to be altered or remedied before they result in abandonment.
| Original language | English |
|---|---|
| Title of host publication | KDD'16 |
| Subtitle of host publication | Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Editors | Charu Aggarwal, Alex Smola |
| Place of Publication | New York NY USA |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 13-20 |
| Number of pages | 8 |
| ISBN (Print) | 9781450342322 |
| DOIs | |
| Publication status | Published - 13 Aug 2016 |
| Event | ACM International Conference on Knowledge Discovery and Data Mining 2016 - Hilton San Francisco Union Square, San Francisco, United States of America Duration: 13 Aug 2016 → 17 Aug 2016 Conference number: 22nd http://www.kdd.org/kdd2016/ https://dl.acm.org/doi/proceedings/10.1145/2939672 |
Conference
| Conference | ACM International Conference on Knowledge Discovery and Data Mining 2016 |
|---|---|
| Abbreviated title | KDD 2016 |
| Country/Territory | United States of America |
| City | San Francisco |
| Period | 13/08/16 → 17/08/16 |
| Other | KDD 2016, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. |
| Internet address |
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver