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Purpose This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time. Design/methodology/approach Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices. Findings The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth. Originality/value The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.
Mohan et al. (Wed,) studied this question.