A regional analysis of house prices in Greece

Purpose – This paper examines the housing market in Greece after the Global Financial Crisis focusing on regional analysis and urban markets in Athens and Thessaloniki. Design/methodology/approach – The paper employs a dataset of over 70,750 property values from 2007 until 2014 incorporating characteristics variables upon which hedonic models are estimated. These form the bases for calculating value indices for mix adjusted houses/apartments by year and region. The indices are used in a panel model in which regional and economic variables are included as independent variables. Using advances in dynamic panel data modelling, a bias-corrected least squares dummy variable model (LSDVC) is applied. Findings – Results indicate the importance of macroeconomic variables in terms of the role of disposable income and significantly different regional effects. Examining the major urban markets, results indicate significant differences in the response of house values to exogenous demand side influences, consistent with the finding of significant regional differences in the LSDVC. Practical implications –Regional heterogeneity needs to be considered in model estimation. Social implications – Policymakers should consider regional differences to improve policy effectiveness. Originality/value – This is the first paper to use a large sample of residential properties in Greece and apply the LSDVC model to overcome estimation biases

Assessing the accuracy and dispersion of real estate investment forecasts

Existing empirical evidence has frequently observed that professional forecasters are conservative and display herding behaviour. Whilst a large number of papers have considered equities as well as macroeconomic series, few have considered the accuracy of forecasts in alternative asset classes such as real estate. We consider the accuracy of forecasts for the UK commercial real estate market over the period 1999-2011. The results illustrate that forecasters display a tendency to under-estimate growth rates during strong market conditions and over-estimate when the market is performing poorly. This conservatism not only results in smoothed estimates but also implies that forecasters display herding behaviour. There is also a marked difference in the relative accuracy of capital and total returns versus rental figures. Whilst rental growth forecasts are relatively accurate, considerable inaccuracy is observed with respect to capital value and total returns

Rationality and momentum in real estate investment forecasts

This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product(GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The results also indicate that forecasters are affected by adverse conditions. This is consistent with the finding that they tend to exhibit greater forecast error when the property market is underperforming and vice-versa

A comparative analysis of the accuracy and uncertainty in real estate and macroeconomic forecasts

We compare and contrast the accuracy and uncertainty in forecasts of rents with those for a variety of macroeconomic series. The results show that in general forecasters tend to be marginally more accurate in the case of macro-economic series than with rents. In common across all of the series, forecasts tend to be smoothed with forecasters under-estimating performance during economic booms, and vice-versa in recessions We find that property forecasts are affected by economic uncertainty, as measured by disagreement across the macro-forecasters. Increased uncertainty leads to increased dispersion in the rental forecasts and a reduction in forecast accuracy

Buyer behaviour and price expectations: a spatial analysis of the Athens residential market

Purpose:This paper examines the price setting behaviour over time and space in the Athens residential market. In periods of house price inflation asking prices are often based upon the last observed highest selling price achieved for a similar property in the same micro-location. However, in a falling market, prices may be rigid downwards and less sensitive to the most recent transaction prices, weakening spatial effects. Furthermore, the paper considers whether future price expectations affect price setting behaviour. Design/methodology/approach: The paper employs a dataset of approximately 24,500 property values from 2007 until 2014 in Athens incorporating characteristics and locational variables. The authors begin by estimating a baseline hedonic price model using property characteristics, neighbourhood amenities and location effects. Following this, a spatio-temporal autoregressive (STAR) model is estimated. Running separate models, the authors account for spatial dependence from historic valuations, contemporaneous peer effects and expectations effects. Findings: The initial STAR model shows significant spatial and temporal effects, the former remaining important in a falling market contrasting with previous literature findings. In the second STAR model, whilst past sales effects remain significant although smaller, contemporaneous and price expectations effects are also found to be significant, the latter capturing anchoring and slow adjustment heuristics in price setting behaviour. Research limitations/implications: As valuations used in the database are based upon comparable sales, then in the recessionary periods covered in the dataset, finding comparables may have become more difficult, and hence this, in turn, may have impacted on valuation accuracy. Practical implications: In addition to past effects, contemporaneous transactions and expected future values need to be taken in consideration in analysing spatial interactions in housing markets. These factors will influence housing markets in different cities and countries. Social implications: The information content of property valuations should more carefully consider the relative importance of different components of asking prices. Originality/value: This is the first paper to use transactions data over a period of falling house prices in Athens and to consider current and future values in addition to past values in a spatio-temporal context

Automatic Mass Valuation for Non-Homogeneous Housing Markets

In recent years big financial institutions are interested in creating and maintaining property valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensive manner and provide some indication for the uncertainty around this forecast. The need for unbiased, objective, systematic assessment of real property has always been important. This need is urgent now as banks need assurance that they have appraised a property on a fair value before issuing a loan and also as the government needs to know the fair market value of a property in order to determine accordingly the annual property tax. In this study we compare various linear, nonlinear and machine learning approaches. We apply a large set of variables, supported by the literature, describing the characteristics of the real estate properties as well as transformation of these variables. The final set consists of 60 variables. We answer the question of variables selection by extracting all available information with the use of several shrinkage methods, machine learning techniques, dimensionality reduction techniques and combination forecasts. The forecasting ability of each method is evaluated out-of-sample is a set of over 30,000 real estate properties from the Greek housing market which is both inefficient and non-homogeneous. Special care is given on measuring the success of the forecasts but also on identifying the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve forecasting accuracy

Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

In this paper we develop an automatic valuation model for property valuation using a large database of historical prices from Greece. The Greek property market is an inefficient, non-homogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem. The available data covers a big range of properties across time and includes the Greek financial crisis period which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and nonlinear models based on regression, hedonic equations, spatial analysis and artificial neural networks. The forecasting ability of each method is evaluated out-of-sample. Special care is given on measuring the success of the forecasts but also to identify the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve performance. Our results indicate that the proposed methodology constitutes an accurate tool for property valuation in non- homogeneous, newly developed markets

Do macro-forecasters herd?

We show that typical tests of whether forecasters herd will falsely indicate herding behaviour for a variety of types of behaviour and forecasting environments that give rise to disagreement amongst forecasters. We establish that forecasters will appear to herd if di¤erences between them reject noise as opposed to private information, or if they arise from informational rigidities. Noise can have a behavioural interpretation, and if so will depend on the behavioural model under consideration. An application of the herding tests to US quarterly survey forecasts of inflation and output growth data 1981-2013 does not support herding behaviour