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Based on the financial data selected from 77 companies during four quarters in 2009 in the comprehensive-class listing companies, two methods, including normal analysis and panel-data model analysis are used to analyze deeply the path of optimization in corporate asset structure. The empirical analysis comes to a memorable conclusion that path-dependence is the character of asset structure optimization. Enterprise asset structure is not only dynamic, but also depended on the balance of profitability and mobility made by managers of enterprise.

We maintain that the appropriate definition of submarkets depends on the use to which they will be put. For mass appraisal purposes, submarkets should be defined so that the accuracy of hedonic predictions will be optimized. Thus we test whether out-ofsample hedonic value predictions can be improved when a large urban housing market is divided into submarkets and we explore the effects of alternative definitions of submarkets on the accuracy of predictions. We compare a set of submarkets based on small geographical areas defined by real estate appraisers with a set of statistically generated submarkets consisting of dwellings that are similar but not necessarily contiguous. The empirical analysis uses a transactions database from Auckland, New Zealand. Price predictions are found to be most accurate when based on the housing market segmentation used by appraisers. We conclude that housing submarkets matter, and location plays the major role in explaining why they matter.

This paper compares alternative methods of controlling for the spatial dependence of house prices in a mass appraisal context. Explicit modeling of the error structure is characterized as a relatively fluid approach to defining housing submarkets. This approach allows the relevant submarket to vary from house to house and for transactions involving other dwellings in each submarket to have varying impacts depending on distance. We conclude that—for our Auckland, New Zealand, data—the gains in accuracy from including submarket variables in an ordinary least squares specification are greater than any benefits from using geostatistical or lattice methods. This conclusion is of practical importance, as a hedonic model with submarket dummy variables is substantially easier to implement than spatial statistical methods

This paper studies actual (real) house prices relative to fundamental (real) house values. Such a focus is warranted since housing constitutes a large fraction of most household portfolios, and its characteristics are such that, in contrast to what prevails in financial markets, arbitrage will be limited and hence correction toward 'true' value is likely to be a prolonged process. Using UK data and a time-varying present value approach, our results preclude the existence of an explosive rational bubble due to non-fundamental factors. We further find that intrinsic bubbles have an important role to play in determining actual house prices although price dynamics appear to impact, particularly in periods of strong deviation from fundamental value. Price dynamics are found to be driven by momentum behaviour. Copyright 2006 The Authors Journal compilation (c) 2006 Blackwell Publishing Ltd.

No abstract

The aim of this paper is to compare various real estate valuation models and the manner in which they take into account environmental variables. The reference model is taken to be a standard linear regression model including ordinal variables to measure environmental quality. This type of model is widely used. It is first compared to linear models which incorporate environmental quality notes extracted from the urban habitat database of a Geographic Information System (GIS) which has been developed recently for Geneva, Switzerland. We also incorporate these quality notes in a single input parameter, a so-called geo-index. The price indices constructed in this way are quite similar to the more traditional hedonic model.We additionally find that Artificial Neural Network (ANN) models, which are non-linear per se, exhibit a similar general form of the price indices, but that the detailed price behaviours of different models feature notable differences depending on the input choice of environmental variables.-2 -

Investment returns, Real estate, Inflation hedging, Error correction model,