Housing Forecasts via Stock Market Indicators

TL;DR

This paper explores the use of stock market technical indicators, such as MACD, RSI, and candlestick patterns, to predict future housing market trends in the USA, demonstrating their varying significance across different market conditions.

Contribution

It extends stock market indicator analysis to the housing market using housing data as candlesticks, highlighting their predictive power and differences in significance between bullish and bearish signals.

Findings

Bearish indicators are more statistically significant than bullish ones.

Indicators show different predictive strengths across stable, volatile, and saturated markets.

Bearish trends are more prominent in less stable or more populated countries.

Abstract

Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.