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How useful are quantitative techniques for the risk analysis/management of a open real estate fund? I am thinking about an approach for Europe (US and other markets are probably quite different - usually more transparent).

There are several factors for the valuation of an object in the fund:

  1. Income stream from rent payments.
  2. Valuation of the sales prices of the object.

The data basis for (2) is certainly an issue. Do people use historical simulation or Monte Carlo approaches? What are your experiences in this field? Can you point out any good references?

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First of all, usually these models are heavily adapted to a specific country (even for Europe), real estate class (housing, commercial) and market (secondary, primary). In general I would say it's very hard to directly apply standard quantitative tools (like MC) from finance for real estate market.

The models I've seen were not heavily quantitative. The most common approach is to build an equilibrium (supply/demand) model which also accounts for macroeconomic factors (interest rates, exchange rates, etc). For example, people try to estimate and forecast the purchasing power (credit availability) and capacity of new development projects. This may include many subtle things as modelling possible delays in development, taxes, policies, etc. Ideally liquidity and credit risks should be taken into account. This approach to modelling and forecasting is sometimes called top-down/bottom-up:

Top-down and bottom-up approaches to forecasting are commonly used in the real estate industry. Macroeconomic (top-down) factors, such as employment growth, gross domestic product (GDP), household formation, and median household income drive both space-using demand and long-term supply. Market construction pipeline data (bottom-up) provides short-term supply information. Current vacancy (bottom-up) is assessed, while future vacancy is derived from forecasted demand, supply, and estimated total market inventory. Current rent (bottom-up) is surveyed, while rent growth is forecast based on forecasted demand and vacancy. Quantitative models built on long-term trends generate baseline results, while adjustments are made to incorporate local knowledge and short-term phenomena.

from Active Private Equity Real Estate Strategy

An important feature that one should account for while doing quantitative modelling in real estate is auto-correlation. Many researchers have documented the unusually strong predictable auto-correlation component. However, as I mentioned before, due to numerous unique features of the market this is hard to exploit on practice.

Also I'm aware about GIS-based and spatial models (e.g. spatial autocorrelation) for valuation of the real estate objects. But I've never seen them implemented in practice.

There are some articles on application of Monte Carlo methods in real estate valuation:

However, I cannot comment on how good they are.

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  • $\begingroup$ Thanks for your answer. I am aware of the auto-correlation. This is often reported in the general setting of alternative investments (where one looks e.g. at private equity, Hedge Funds and Real estate) too. I am aware that the field is large and extremely subtle but I hope that some remarks as yours come in. Any references for further investigations would be appreciated. $\endgroup$
    – Richi Wa
    Feb 4, 2013 at 11:31
  • $\begingroup$ Thank you for finding those references! This is already some reading material. Let's see whether new ideas come in - if not I would accept your nice answer. $\endgroup$
    – Richi Wa
    Feb 4, 2013 at 13:32

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