Designing policy recommendations to reduce home abandonment in Mexico

Klaus Ackermann, Eduardo Blancas Reyes, Sue He, Thomas Anderson Keller, Paul Van Der Boor, Romana Khan

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

2 Citations (Scopus)

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 languageEnglish
Title of host publicationKDD'16
Subtitle of host publicationProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsCharu Aggarwal, Alex Smola
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages13-20
Number of pages8
ISBN (Print)9781450342322
DOIs
Publication statusPublished - 13 Aug 2016
EventACM International Conference on Knowledge Discovery and Data Mining 2016 - Hilton San Francisco Union Square, San Francisco, United States of America
Duration: 13 Aug 201617 Aug 2016
Conference number: 22nd
http://www.kdd.org/kdd2016/
https://dl.acm.org/doi/proceedings/10.1145/2939672

Conference

ConferenceACM International Conference on Knowledge Discovery and Data Mining 2016
Abbreviated titleKDD 2016
CountryUnited States of America
CitySan Francisco
Period13/08/1617/08/16
OtherKDD 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

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