| With the development of the urbanization process and the reformation of the housing system in China, the construction for housing is going to be a new basepoint for the development of the Chinese GDP. On the other hand, the social focus on how to achieve the goal of "every family has a residence to live in" has also been driving the development of urban housing. Nevertheless, price is the dominant factor affecting the effectiveness of urban housing development. Consequently, the urban housing price has caused increasing concerns to the Chinese government, the real estate developers in the Chinese real estate market and the Chinese citizens. Whilst there exist many problems in the current Chinese housing market, it appears that the major problems include the excess investment in real estate industry, the excessively high housing price, lack of accessibility of market information, and the shortage of housing supply to middle or low income family. All these major problems relate to housing price. Housing price has been rising in most of the urban areas in China over last decade. For example, since 1998 the housing price in Hangzhou has been rising continuously, called the "Hangzhou phenomena", which has all typical characteristics of the Chinese real estate market.As housing price is affected by spatial distribution and time sequence in very different ways, it is considered of vital importance to examine the spatial distribution of urban housing price and the evolvement of this distribution along the time sequence. This examination will help to analyze whether the "Hangzhou phenomena" is reasonable, consequently, the reasonable and unreasonable aspects can be identified. The findings about the law of spatial distribution of urban housing price and its evolvement in this research has both theoretical and practical value to help the government to formulate effective policies for regulating the housing price, supplying sufficient housing, identifying the effective market demand, controlling the investment structure of real estate business, and helping the real estate developers and customers to participate in themarket operation at proper price level.Whilst this study built up a theoretical foundation on a broad base, the data used for analysis refers to the housing practice in Hangzhou of Zhejiang province. The data are planned for collection mainly under three groups: the price for housing exchange in the primary market, the price for housing exchange in the second market, and the price of leasehold housing in different time. Major factors affecting housing price are investigated. It was emphasized to integrate the factors of space and time in this study. To reveal the law of spatial distribution of urban housing price and its evolvement, the approach such as data visualization, hedonic model, Geo-statistics was used based on GIS.The collection of effective data is considered essential in the study for ensuring the quality of the analysis results. To satisfy the specimen requirements for both quality and quantity, the data are collected from the quoted price for housing exchange on advertisements in the second market. The adequacy of the price data is proven by comparing to the data from the sample survey to the bargaining price in the reality. Assistance was successfully obtained from six major real estate agencies in Hangzhou for collecting the quoted price on advertisements for second housing exchange. The analysis on the area of housing unit distribution frequency collected from the six agencies demonstrates that the frequency histograms have the similar shape. This further supports the reliability of data. In total, 30567 data specimen were collected, of them 28645 are effective and used for analysis in the study. The data samples are considered effective and comprehensive.By using the data collected from the housing market, this study has established an urban housing spatial information database, and data in the database are geocoded with the aid of GIS according to detail-described space and spatial location. The use of GIS technique provides the spatial location of the geocoded specimen in a map, which registers 9361 specimen with specific spatial coordinate. Estimations are given to the features of housing location, the out-going convenience, the surrounding environment and the nearby infrastructure conditions by the analysis of buffer, overlay, network based on GIS technique.Visualized analysis was conducted by histogram and scatterplot on a number of parameters concerning a particular housing unit, including the date when the building was built, the size ofthe area, quoted total price and quoted unit price of the sample housing points. By grouping housing building according to construction period, analysis results are obtained across a range of aspects. For example, (1) the urban housing specimen in Hangzhou after 2000 account for 34.62% of the total specimen collected, with the expectation for a great number more to come in 2005. It has been found that those newly built houses can quickly go to the second housing exchange market, indicating that those who bought houses are for mainly for the purpose of investment instead of living. (2) Over half of the urban houses built in Hangzhou after 2000 have a size of more than 120 m2 while the median size of those houses before 2000 is only 61 m2. On the positive side, this shows that the living conditions of the citizens in the city have been greatly improved. Nevertheless, it also raises the concern how such size-increasing trend in developing housing can sustain if considerations is also given to the limited land resource. (3) In Hangzhou city, the median quoted total price for a housing unit if housing were built before 2000 was RMB417,000 while that for a housing unit if housing were built after 2000 reaches to RMB900,000. Usually, the ratio of the property price to income is used to analyze the citizens' purchase power. The ratio of the property price to income used to be 12.08:1 if housing were built before 2000, whilst it has been already to 26.08:1 if housing were built after 2000, far beyond the 6:1 precaution-line defined in previous studies.The Hedonic Model is applied in analyzing the impacts of various housing features on housing price. Housing features are investigated under four categories: construction features, location features, surrounding features, and time features. Under this classification, construction features are mainly described by the further specific features including the size of construction area, numbers of rooms and halls in a housing unit, date of construction, date of permission for occupancy, and the floor order number. Location features are described by the distance between the housing unit to the West Lake and that to the CBD. The surrounding features are characterized by the density of roads, the nearby bus stops, the transportation alternatives, the surrounding environment, the nearby school and hospital. And the time features refer to the date that the quoted price is collected. Therefore, in total 18 specific features are taken into account in using the Hedonic Model.The application of Hedonic Model in this study is proven effective, demonstrating that the status of the urban housing price in Hangzhou can be explained by analyzing various housing features.It appears, of all the 18 housing features, the size of construction area has the most important influence and plays as the determinant factor to the total price for a housing unit. The location features also have significant influence on the housing price, with the evidence that the price decreases when of the distance between the houses to the CBD or the West Lake increases. The construction time of the housing building also affects the house price. The time of quoted price applied in the model is of vital importance, next to the size of construction area, which explains the reason why the price changes greatly with time. As far as the floor feature is concerned, a coefficient is used to adjust the prices for different housing units which are in difference between different floors, following the common practice that a unit in higher floor has a higher price that the one in lower floor. It seems that the influence of the surrounding features on housing price depends on subjective judgment. In general, the density of roads, the nearby bus stops, the transportation alternatives, and the location of nearby university or a hospital have a positive influence to the price while the location nearby a train station has a obviously negative effect on the price. It is also interesting to note that that the location near a river or a primary school has a negative influemce to the housing price while the location near a high school does not have obvious influence to the price.In another key element, this study applies the Geostatistics to establish the spatial database of housing price in different time periods. The year 1999 and 2004 are used as the start and report time respectively. By using the data in the spatial database, spatial relativity and spatial distribution features of the housing price can be analyzed through using spatial statistics methods such as spatial continuity analysis, h-scatterplots analysis, and semi-variance analysis. The technique of Kriging is used to help develop the map of housing price in 1999 and 2004. With the aid of the GIS based overlay approach, the spatial information can be obtained from the map, and analysis can conducted on these data to reveal the law of spatial distribution of housing price that changes with time.According to the findings from this research, the urban housing price in Hangzhou in 1999 decreases when the location of the housing unit spreads out from the West Lake as the city center, and the price increases in these areas around the west of the city and Qiantang river, which become increasingly attractive and play as sub-center of the city. The distribution of the housing price remains the same to large extent in Hangzhou until 2004, centered by West Lake with other...
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