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The literature on investor decision-making behaviour in property investment is sparse, loosely integrated and focused principally upon large, institutional investors. It reflects rational, normative models that treat investor behaviour as highly structured and formalised. By contrast, behavioural psychology suggests that individuals frequently act sub-optimally. These ideas have been explored in financial decision making and have been found in the actions of property valuers and property lenders. This paper addresses this neglected area of property investment decision-making. Semi-structured interviews were conducted with a sample of property investment directors of smaller property companies. The interviews investigate the decision-making structures in these companies, the process by which investment strategy is formulated, the investment``screening'' process and the determinants of purchase/sell decisions. The findings are discussed and related to the literature on decision making under uncertainty. IntroductionIn large part, the literature on business decision making is dominated by rationalist perspectives and focused on examination of sets of rules that people should follow, rather than how decisions are actually made. The limited amount of investigation of property decision making (e.g. Anderson and Settle, 1996;Farragher and Kleinman, 1996;Miles et al., 1989) has likewise been concerned principally with the rules and techniques that people adopt, with normative models ubiquitous in textbook descriptions of the decision process (Pyhrr et al., 1989;Hartigay and Yu, 1993;Jaffe and Sirmans, 1995). The general dominance of rationalist approaches has been challenged by behavioural decision theory, drawing from cognitive psychology, and addressed at closer examination of process features, including the decision environment and individual differences between decision makers (Tversky and Kahneman, 1974). The impact of behavioural approaches upon property analysis has so far largely been confined to valuation decisions

Property decision-making is typically characterized as a structured rational process, using factual data and leading to optimal decision-making. To augment, or substitute for de ciencies in, such data, property investors may turn to perceptions of investor or market sentiment. Reliance on sentiment in the wider nancial markets is, however, regarded as suboptimal behaviour that leads to mispricing. Discussion of these contrasting views of sentiment is coupled with the results from a survey of property investment decision-makers. These results indicate that investor sentiment is an important factor in property decision-making, despite its neglect in formal explanations of property market functioning. The conception of investor sentiment held by survey respondents is explored and con rmed as different to the concept applied in the wider nancial markets.

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This paper is concerned as to whether it is more appropriate to use aggregate or disaggregate models in forecasting house prices using hedonic modelling. It is accepted that the implicit pricing of some of the attributes is not stable between locations, property types and ages but it is argued that this can be effectively modelled with an aggregate method. The models are developed using a dataset of nearly 18,000 transactions in the UK Midlands region in 1994. The comparative performance of these models is then considered using two approaches. Chow tests of the error differences between actual price and the price predicted by the models suggest that the submarket models lead to statistically significant, though small, improvements. A second approach, using comparison of the root mean square errors, is conducted on the models' forecasts for a 10 per cent sample of nearly 2,000 transactions excluded from the modelling process. This shows little practical difference in the forecasting ability between the two approaches. Great care needs to be taken over sample size if a disaggregate model is used.

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IntroductionMultiple regression analysis (MRA) is a technique which seeks to link the value of a number of independent variables to a further variable whose value is supposedly dependent on them (i.e. the "dependent" variable). The intention is to produce a model, or equation, which will explain this relationship and hence enable prediction of the dependent variable in cases when this is unknown. Judgements about which model best achieves this aim are based on statistical tests. No attempt is made here to describe the technique fully, since its basis and the problems which may accompany it are well documented elsewhere (e.g.