Humanizing Finance by Hedging Property Values

Abstract

Abstract:The recent financial crisis triggered the greatest recession since the 1930s and had a devastating impact on households' wealth and on their capacity to reduce their indebtedness. In the aftermath, it became clear that there is significant room for improvement in property risk management. While there has been innovation in the management of corporate finance risk, real estate has lagged behind. Now is the time to expand the range of tools available for hedging households' risks and, thus, to advance the democratization of finance. Property equity represents the major asset in households' portfolios in developed and undeveloped countries. The present paper analyzes a set of potential innovations in real estate risk management, such as price level-adjusted mortgages, property derivatives, and home equity value insurance. Financial institutions, households, and governments should work together to improve the performance of the financial instruments available and, thus, to help mitigate the worst impacts of economic cycles.

A real-time mix-adjusted median property price index enabled by an efficient nearest neighbour approximation data structure

“…Due to the recent global financial crisis, house price indexes and forecasting models play a more crucial role than ever. The key to providing a more robust and up-todate overview of the housing market lies in machine learning and statistical analysis on set of big data [7]. The primary aim is the improvement of currently popular algorithms for calculating and forecasting price changes, while making such indexes faster to compute and more regularly updated.…”

“…Hedging against market risk has been shown to be potentially beneficial to all stakeholders, however, it relies on having up-to-date and reliable price change information which is generally not publicly available [7,10]. This restricts the possibility of this tool becoming a mainstream option to homeowners and small businesses.…”

A rapidly updating stratified mix-adjusted median property price index model

“…Due to the recent global financial crisis, house price indexes and forecasting models play a more crucial role than ever. The key to providing a more robust and up-to-date overview of the housing market lies in machine learning and statistical analysis on set of big data [7]. The primary aim is the improvement of currently popular algorithms for calculating and forecasting price changes, while making such indexes faster to compute and more regularly updated.…”