Search results

The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.

The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression, random forests, gradient boosting and neural networks) and two updating methods (moving and extending windows). They use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017.

The gradient boosting method yields the greatest accuracy, while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results.

This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms and regulatory authorities.

In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the most appropriate model for risk management practice.

The study considers a variety of models, including those typically employed within the risk management industry, such as averaging and smoothing techniques, as well as those favored in academic circles, such as the GARCH genre of models, and a more recent realized volatility approach which incorporates both the simplicity in construction favored by the finance industry and the flexibility and theoretical underpinnings recommended by academics.

The results support the view that this realized volatility measure provides not only superior volatility forecasts per se, but also allows for improved hedge ratio and VaR calculations.

The research findings carry practical implications for the conduct of risk management, namely that volatility forecasts are best obtained using the realized volatility approach.

It is therefore proposed that a future direction for risk management practice may be to utilize such measures, while more generally it is hoped that such approaches may improve the cross‐fertilization of ideas and practice between the academic and practitioner communities.

The failure of the efficient market hypothesis has a direct bearing on the Geometric Brownian Motion model of asset returns. The current paper aims to investigate the effect that the autocorrelation in the time‐series of returns has on the calculation of expected shortfall (ES) for an asset‐liability investor.

The regression model is selected according to the Akaike and the Schwarz information criterion. A series of tests are used to insure the stability of the autocorrelation parameters. Autocorrelation‐adjusted formulas for volatility and cross‐asset correlations are then employed for the computations.

The presence of autocorrelation changes the values of most of the correlation parameters used in the calculation of the ES of the risk bearing capital (RBC) – in some cases the cross‐asset correlation parameters double. Once the presence of smoothing is accounted for, the ES increases by 1 per cent in relative value.

Other asset classes may also feature smoothed time‐series requiring thus an account of their autocorrelation structure and their interaction with the property asset. An analysis of the time stability of the cross‐asset correlations may also improve the estimation of the optimal RBC.

The proposed method focuses on the proper calculation of the RBC through the judicious estimation of the relevant risk measure for an investor who, while not having access to the underlying data pool from which the property index is computed, cannot adjust the index for the potential presence of temporal aggregation and market illiquidity.

Investment in Australia’s property market, whether directly or indirectly through Australian real estate investment trusts (A-REITs), grew remarkably since the 1990s. The degree of segregation between the property market and other financial assets, such as shares and bonds, can influence the diversification benefits within multi-asset portfolios. This raises the question of whether direct and indirect property investments are substitutable. Establishing how information transmits between asset classes and impacts the predictability of returns is of interest to investors. The paper aims to discuss these issues.

The authors study the linkages between direct and indirect Australian property sectors from 1985 to 2013, with shares and bonds. This paper employs an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to de-smooth a valuation-based direct property index. The authors establish directional lead-lag relationships between markets using bi-variate Granger causality tests. Johansen cointegration tests are carried out to examine how direct and indirect property markets adjust to an equilibrium long-term relationship and short-term deviations from such a relationship with other asset classes.

The authors find the use of appraisal-based property data creates a smoothing bias which masks the extent of how information is transmitted between the indirect property sector, stock and bond markets, and influences returns. The authors demonstrate that an ARFIMA process accounting for a smoothing bias up to lags of four quarters can overcome the overstatement of the smoothing bias from traditional AR models, after individually appraised constituent properties are aggregated into an overall index. The results show that direct property adjusts to information transmitted from market-traded A-REITs and stocks.

The study shows direct property investments and A-REITs are substitutible in a multi-asset portfolio in the long and short term.

The authors apply an ARFIMA(p,d,q) model to de-smooth Australian property returns, as proposed by Bond and Hwang (2007). The authors expect the findings will contribute to the discussion on whether direct property and REITs are substitutes in a multi-asset portfolio.

The purpose of this paper is to fill an existing gap in the field. A transaction-based hotel price index for Europe is constructed to provide a true measure for hotel real estate performance. The index will enable investors enhance investment decisions in many ways: to assess individual property performance; to make an objective decision about where to invest and in which property type; to assess the relative performance of hotel assets to all other sectors and consequently reach optimal funds allocation decisions. This will allow investors to time their acquisitions/disposals according to the hotel property cycle.

Data include 495 hotel property transactions in Europe during the period between 2004 and 2013. Transaction prices and property characteristics were collected from a variety sources published by hotel agents and consultants, property magazines, newspapers, tourist board, individual property and hotel association registers and web sites. Data include property name, sale price, size, time of sale, location, buyers and sellers. A hedonic pricing model is developed where the transaction price is regressed on the different characteristics. The index is calculated by taking the anti-logs of regression coefficients of the year index.

This paper claims that the hotel property price index (HPPI) portrays a more realistic picture of what happened to hotel property prices in 2008 showing a single digit negative growth vs the hotel valuation index which reports a double digit negative growth rate in European hotel prices during the same year. The real impact of recession showed on hotel property prices in 2009. HPPI shows a crash in hotel property prices by -23.7 per cent in 2009. The year 2011 was marked by more sales transacted through administrators and a looming double-dip recession. Unlike appraisal-based indices, HPPI does not suffer from sticky valuation issues and is not desensitise from distressed properties. Therefore, it was more volatile to distressed situations throughout the period between 2011 and 2013.

Results of this study should be considered with caution. There are limitations associated with transaction data including incompleteness or inaccuracies regarding price data, financing information for each deal, property tenure, and property characteristics.

This work has successfully developed an HPPI for hotel property in Europe. This paper paves the way for transaction-based indices that are more volatile than existing appraisal-based indices. This represents a significant development in tracking price movements of hotel properties in Europe. The index has potential to support research and forecasting of the hotel property cycles.

This paper fulfils an identified need to track hotel property prices and timing the hotel property cycle.

Are share markets too volatile? While it is difficult to ignore share market volatility it is important to determine whether volatility is excessive. This paper replicates the Shiller (1981) test as well as applying standard time series analysis to annual Australian stock market data for the period 1883 to 1999. While Shiller’s test suggests the possibility of excess volatility, time series analysis identifies a long‐run relationship between share market value and dividends, consistent with the share market reverting to its fundamental discounted cash flow value over time.

Tests for correlation between the NCREIF (NC) Index and EREIT Index. A multiple time series methodology is used to control for spurious correlation, allow for leading and lagging relationships, and to control for autoregressive moving average processes found in the time series. The underlying variables generating returns for the investor, current cash flow and capital appreciation, are analysed separately. Significant correlation is found between the NC cash flows and EREIT dividends. Significant correlation is not observed between the NC portfolio and EREIT when capital values are analysed. Suggests that one or both series are not a good measure of real estate returns.

Price indices for commercial real estate markets are difficult to construct because assets are heterogeneous, they are spatially dispersed and they are infrequently traded. Appraisal-based indices are one response to these problems, but may understate volatility or fail to capture turning points in a timely manner. This paper estimates “transaction linked indices” for major European markets to see whether these offer a different perspective on market performance. The paper aims to discuss these issues.

The assessed value method is used to construct the indices. This has been recently applied to commercial real estate datasets in the USA and UK. The underlying data comprise appraisals and sale prices for assets monitored by Investment Property Databank (IPD). The indices are compared to appraisal-based series for the countries concerned for Q4 2001 to Q4 2012.

Transaction linked indices show stronger growth and sharper declines over the course of the cycle, but they do not notably lead their appraisal-based counterparts. They are typically two to four times more volatile.

Only country-level indicators can be constructed in many cases owing to low trading volumes in the period studied, and this same issue prevented sample selection bias from being analysed in depth.

Discussion of the utility of transaction-based price indicators is extended to European commercial real estate markets. The indicators offer alternative estimates of real estate market volatility that may be useful in asset allocation and risk modelling, including in a regulatory context.

This study aims to propose and test an innovative methodology for assessing mortgage lending value. The method tries to improve and rationalize, within the canonical and derivative approach that is generally used by the sector operators, the appraisal of the percentage reduction to be applied to the market value.

Considering that the European Mortgage Federation and the Basel Committee highlight the importance of information about the risks of properties to be loaned on, the value at risk approach has been developed so as to assess the mortgage lending value as a technique of risk analysis. With reference to the Italian context, the method elaborates the historical analysis of the property values in 93 major Italian cities for the residential and commercial intended uses in a significant period (1967-2015) and allows to determine the reduction coefficients of the market value as a function of the central, semi-central and peripheral locations of the property.

The results include the reduction coefficients of the market value for the derivative appraisal of the mortgage lending value. The coefficients obtained satisfy the need for a rational assessment of the property risk and the appropriate spatial contextualization of the risk components related to the local demand and supply, thus eliminating any inconsistency and danger of determining the mortgage lending value using a simple and lump-sum percentage deduction of the market value.

The global economic crisis in the past decade, triggered by the 2007 US Subprime mortgage crisis and consequent collapse of property values, has highlighted the need for high level professional skills in the appraisal of properties as securities for credit exposures. The method proposed for the assessment of the mortgage lending value allows to overcome the uncertainties underlying the determination of an independent value through indirect methods (income approach, cost approach) and rationalize the appraisal of the risk in the traditional derivative approach through a flexible procedure, with it being possible to adapt it to any territorial context, as well as any intended use.

This paper aims to analyze the statistical characteristics of changes in property forward prices. As highlighted in a survey conducted at the MIT Center for Real Estate in 2006, the relatively weak understanding in their prices is one of the most important barriers in their use. In this context, the analysis of the forward price term structure is essential. Do the short- and long-term forward prices behave similarly? Do property derivatives behave like other derivative assets or other related assets? This study also investigates the lead–lag relationship between spot and forward returns for different maturities.

Using four years and nine months of data on the UK Investment Property Databank (IPD), all property total return swaps are examined. We strip the swaps into their forwards and study their statistical characteristics (the first four moments and their autocorrelation levels). The relationships among the forward contracts, the underlying asset (IPD index and IPD unsmoothed) and other assets (risk-free rate, listed real estate) are explored. Using the Yiu et al. (2005) methodology, the lead–lag relationship between the spot and the forwards is assessed.

The index appears to be significantly less volatile and less efficient, in terms of correlation than its own derivative contracts. Moreover, changes in forward prices are leading indicators of the IPD index. Their risks tend to converge with the implied volatility of the REIT’s operating asset but without being affected by the general stock market risks. Regarding the forward price–discovery function, investors should collect information not only from the spot market but also, maybe primarily, from the derivative market.

In this paper, we use a never-exploited database that is relative to the quotes of the UK IPD swaps. It is the first attempt to analyze the statistical characteristics of their changes. Our results show that these prices are clearly superior to the spot series, in terms of risks but without behaving affected by the tyranny of the past values. These findings may conduct to consider new methods to unsmooth current real estate indices. Characterized by a strong sensitivity to the changes in the information set, property derivative-based indicators should lead to increased efficiency in the spot market.