Asset-Specific Forecasting: Integrating Qualitative & Quantitative Methods

Asset-Specific Forecasting: Integrating Qualitative & Quantitative Methods

The Importance of Asset-Specific Forecasting

While understanding broader market trends is crucial, successful real estate investment hinges on accurate asset-specific forecasting. This involves tailoring forecasting methods to the unique characteristics and circumstances of an individual property, considering its location, type, condition, and planned interventions like refurbishment.

Formal vs. Informal Forecasting Approaches

Forecasting methods can be broadly categorized as formal and informal:

• Informal Approaches: Relies on market experience, intuition, and sentiment alongside fundamental market understanding. Prevalent in real estate due to entrepreneurial traditions.
• Formal Approaches: Utilize structured methodologies, including quantitative, qualitative, or a combination of both.

Quantitative Forecasting Methods

Quantitative methods employ mathematical and statistical techniques to analyze historical data and project future trends. There are two primary categories:

• Time Series/Trend-Based Models:

  • These models analyze historical data patterns to predict future values without necessarily establishing causal relationships. They are empirically driven.
  • Suitable for forecasting income returns where historic returns are available.
  • Examples include:
    • Smoothing Models: Such as moving averages or exponential smoothing.
    • Regression Models: Such as autoregressive (AR) models.

  • A general autoregressive model of order q, AR(q), can be represented as:

    TRt = α + β1*TRt-1 + β2*TRt-2 + ... + βq*TRt-q + εt

    Where:
    * TRt is the total return in period t.
    * α is a constant.
    * βi are coefficients for the lagged total returns.
    * TRt-i is the total return in period t-i (lagged values).
    * εt is an error term.
    * Advantages: Simplicity, ease of development, minimal data requirements, and can be useful for establishing a baseline forecast.
    * Disadvantages: Lack of explanatory power, limited ability to predict turning points, relies on the assumption that past trends will continue.

• Causal/Structural Models:

  • These models establish relationships between a dependent variable (e.g., real estate return) and one or more independent variables (e.g., demographics, economic indicators).
  • Requires a strong theoretical underpinning.
  • Ideally, independent variables should lead the dependent variable.
  • Advantages: Explanatory power, ability to model the impact of specific factors.
  • Disadvantages: Data intensive, requires forecasts of independent variables, potentially complex.

  • Single Equation Models: Typically multiple regression models. A general form of a multiple regression model is:

    TRt = α + β1*X1t + β2*X2t + ... + βn*Xnt + εt

    Where:
    * TRt is the total return in period t.
    * α is a constant.
    * βi are coefficients for the independent variables.
    * Xit are independent variables in period t.
    * εt is an error term.
    * Systems of Equations Models: A nested set of equations where the output of one equation serves as an input to another. This allows for a more detailed modeling of the interrelationships between variables, but requires careful consideration of stability and feedback loops.

Qualitative Forecasting Methods

Qualitative methods rely on expert judgment, market surveys, and other subjective techniques to predict future trends.

• Expert Opinion/Surveys of Expert Opinion: Gathering insights from experienced professionals. Provides a market view that reassures investment decisions, even if the forecast is eventually inaccurate.
• Delphi Method: A structured process for soliciting and aggregating the opinions of experts.
• Historical or Geographical Analogy: Drawing comparisons to past events or similar markets.
• Advantages: Ability to capture market sentiment, predict turning points, and incorporate non-quantifiable factors.
• Disadvantages: Subjectivity, potential for bias, reliance on expert judgment which may be flawed, and vulnerable to “anchoring” (over-reliance on current values).

Integration of Qualitative and Quantitative Methods

Integrating qualitative and quantitative methods can produce more robust and reliable forecasts. Qualitative methods can be used to:

1. Determine the appropriate quantitative model.
2. Select the most relevant variables for the model.
3. Adjust model inputs or outputs based on expert judgment.
4. Evaluate the results of quantitative models.

Qualitative Modeling of Yields and Capitalization Rates

Yields and capitalization rates, which are reflections of market sentiment and risk appetite, are often difficult to forecast quantitatively. A qualitative model can be used, such as a Gordon’s growth model adaptation:

K = RFR + RP – G + D

Where:

• K is the yield or capitalization rate.
• RFR is the risk-free rate (e.g., long-term government bond rate).
• RP is the risk premium for investing in real estate compared to the RFR. This is the product of the real estate investment market and depends on the availability of equity and debt and the demand for and supply of properties for sale. Sentiment and momentum greatly influence the RP.
• G is the long-term average rental growth rate.
• D is the long-term average depreciation rate (or annual investment required to maintain the asset).

The risk premium (RP) is highly subjective and is influenced by:

• The availability of equity and debt financing.
• The supply and demand of investment properties.
• The perceived risk of real estate compared to other asset classes.
• Market sentiment and momentum.
• Expert opinion, surveys, Delphic techniques and historical or geographical analogy are all techniques that can help a forecaster estimate an appropriate value against either long-term trends or a life-cycle analysis.

A Conceptual Framework for Forecasting Real Estate Returns

A comprehensive forecasting system comprises three main stages:

1. Modeling Occupational Markets: Use causal/structural quantitative techniques to forecast determinants of Net Operating Income (NOI) to derive the income return.
2. Forecasting Yield/Cap Rate: Employ qualitative techniques to predict yield or cap rate as an input to the calculation of capital value and hence the forecasting of the capital return.
3. Calculating Total Return: Summing the income and capital returns.

Forecasting Demand

Demand forecasting can use a causal/structural model.
For example:

Demand for real estate = f(population, average wealth, inverse of previous period’s price)

Forecasting Supply

• Short-Term (2-3 years): Monitor projects under construction or use causal/structural models (e.g., supply depends on construction permits issued in the past).
• Long-Term (3+ years): Use time-series/trend-based techniques.

Occupancy, Rent, and NOI

Forecasted using a system of equations, incorporating supply and demand. The output is an income return forecast.

Practical Application: Asset-Specific Adjustments

The principal merit of this approach lies in the explicit presentation of the interconnection between the strategic forecast and the asset-level forecast.
For example, consider a scenario where the benchmark market forecast anticipates an 8% return in the next period (T+1). However, a specific asset is undergoing repositioning, expected to attract an uplift in value:
* Benchmark Market Return (T+1): 8%
* Specific Asset Return (T+1): 15%
The comment column permits a clear explanation of why an individual asset may be expected to outperform or underperform the wider market.

Experiment: Sensitivity Analysis of Key Assumptions

To account for the inherent uncertainty in forecasting, investors can conduct sensitivity tests on key assumptions or input variables. For example:
1. Base Case: Develop a baseline forecast for a specific asset, considering all relevant factors.
2. Scenario 1 (Optimistic): Increase the rental growth rate by 1% per year for the next five years.
3. Scenario 2 (Pessimistic): Decrease the occupancy rate by 5% for the next two years due to increased competition.
4. Scenario 3 (Refurbishment Delay): Delay the planned refurbishment by one year, which impacts the expected increase in rental income.
5. Analyze Results: Compare the forecasted returns under each scenario to assess the potential impact of these factors on the asset’s performance.

Conclusion

Integrating quantitative and qualitative methods is essential for developing accurate and reliable asset-specific forecasts. By combining the rigor of quantitative analysis with the insights of qualitative judgment, investors can make more informed decisions and improve their real estate investment outcomes.