Unlike capital markets, real estate tends to be an industry where some students might have the quantitative capabilities to analyze real estate investments but lack literacy and experience. Our objective with this article is not to explain quantitative methods or applied analytics related to real estate but rather present a solid starting point to the reader so that real estate analysis becomes less of a grey area in financial literacy in our community. This article will simplify how forecasting real estate returns are usually done in the real world. Please keep in mind that there is no right or wrong approach in forecasting since much depends on the forecaster’s objective and data availability.
In practice, many organizations take a pragmatic view on forecasting Real Estate Returns. A common yet simplified framework for forecasting Real Estate returns comprises three main stages:
1. A system of equations to model the occupational markets and forecast Net Operating Income determinants (Quantitative techniques) to derive the income return.
2. Qualitative techniques to forecast yield/Capitalisation rate. These results are used to calculate the capital value and hence the forecasting of the capital return.
3. Income Return + Capital Return = Total Return.
Demand
Forecasting real estate demand is usually done with the application of econometric techniques. Although the precise variables will vary with location and sector, a combination of demographic, economic and price variables are likely to form a good starting point in an econometric model. Demographic and economic factors that influence employment tend to be a key influence in many real estate demand models.
For example, it would be reasonable for a forecaster to assume that the demand for real estate is a product of the number of people in a defined trade area and their average wealth and inversely related to the price of real estate in the previous period.
Supply
When characterizing real estate supply, it is essential to separate short term (two to three years) and long term (more than three years) supply.
In the short term, there are mainly two ways through which we can forecast real estate supply. Firstly, we can monitor real estate projects under construction and build a granular database, which is the approach taken by most brokerages to gain a rough idea of what will become available on the market in a few years. Secondly, we can use simple structural techniques where we can, for example, look at the construction permits issued three years ago and calculate new supply based on that.
Over more extended periods of time, however, new supply has to be forecasted using time-series or trend-based techniques, where we estimate new supply by looking at new supply from the previous period.
Conclusion
All in all, although these models seem precise and exact, the statistical techniques are somewhat let down by the lack of reliable and long-term data sets, not only in real estate investment performance but also from the independent variables that help us forecast real estate returns. However, as time goes on and the Real Estate Industry becomes more sophisticated, data tends to become more widely available and cost-effective, and quantitative techniques can be applied more widely, and more data-intensive models can be used. Nevertheless, for the time being, a substantial element of practicality and pragmatism needs to be combined with quantitative models when forecasting real estate returns.
Most of all, it is essential to understand that forecasting is a tool, not a product, and that for now, forecasting real estate returns will remain both a science and an art.
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