Open AccessInternational Real Estate Review
Author(s) -
William Miles
Publication year - 2020
Publication title -
journal of the asian real estate society
Language(s) - English
Resource type - Journals
ISSN - 1029-6131
DOI - 10.53383/100307
Subject(s) - real estate , economics , econometrics , asset (computer security) , measure (data warehouse) , house price , stability (learning theory) , monetary economics , finance , computer science , computer security , database , machine learning
Asset prices and fundamentals can move apart, as is the case during bubble episodes. However, they should exhibit a stable relationship in the long run. For UK housing, previous studies have investigated whether house prices share a long run relationship with income. Results thus far have not yet found such stability in the interaction of the two variables. These previous papers have imposed linear adjustment on the relationship. Nonlinear adjustment, however, has been shown to be a feature in a number of housing market relationships. In this study, we utilize a data set that consists of home prices relative to first time buyer income for the UK and its twelve constituent regions, which gives us a direct measure of affordability. We test for the stationarity of the home price/first time buyer income ratio with linear tests, and, as in past studies, fail to find a long run relationship. However, we then employ a nonlinear test, and find a stationary relationship for the UK and seven of the twelve regions. In particular, the regions closest to London appear most clearly to have a stationary relationship between home prices and income.