An agent-based model for the assessment of LTV caps
Dimitrios Laliotis,
Alejandro Buesa,
Miha Leber and
Javier Población
Quantitative Finance, 2020, vol. 20, issue 10, 1721-1748
Abstract:
We assess the effects of regulatory caps in the loan-to-value (LTV) ratio for housing mortgages using an agent-based model. Sellers, buyers and banks interact within a computational framework that enables the application of LTV caps to a one-step housing market. We first conduct a simulation exercise; later, we calibrate the probability distributions based on actual European data from the Household Finance and Consumption Survey. In both cases, the application of an LTV cap results in a modified distribution of buyers in terms of property values, bidding prices and properties sold, depending on the probability distributions of the LTV ratio, wealth and debt-to-income ratios considered. The results are of similar magnitude to other studies in the literature embodying other analytical approaches, and they suggest that our methodology can potentially be used to gauge the impact of common macroprudential measures.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (10)
Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2020.1733058 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: An agent-based model for the assessment of LTV caps (2019) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:20:y:2020:i:10:p:1721-1748
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20
DOI: 10.1080/14697688.2020.1733058
Access Statistics for this article
Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral
More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().