Research On Guangzhou Property Service Price Index

The industry of property management has developed for more than 30 years in our country and got a huge progress in all aspects, the industry has influenced economy and people’s life increasingly. However, there’re also a series of problems in property management when it developed in our country. One of the key problems is that there is no scientific mechanism to regulate the property service price, property service price is not in line with the change of property service cost. Therefore, it is essential to build scientific property service price index and make it a scientific and objective basis to regulate the price of property service.In this paper, from the features of property service, the author utilizes the other related research results, tries to build a perfect price index system and reasonable methods and produces initial price index of property service in Guangzhou in sample of parts of living property service program price in Guangzhou.In this paper, firstly, the author concludes the background, aim, meaning, structure, methods, train and innovation point of the research. Secondly, extensive access to the literature of price index at home and abroad, concludes the definition, index system and methods(data collection, math model, weight, base period and report period) of each price index from the research on the production of price index in products and services industry at home and abroad. In the third part and fourth part, draws successful lessons from the relevant industry building index system and the model with the features of property service industry, introduces the structure of property service price index system and the methods(data collection, math model, weight, base period and report period) to produce property service price index. In the fifth part, on the basis of building the index system and methods, the author designs data collection charts, with the agreement of real estate management department in Tianhe district in Guangzhou, collects the prices and building area of residential real estate from 2010 to 2014 with independent third party identity, uses Excel and SPSS to analyse the data, applies the data to the math model, calculates the price index and analyses the relationship among price indexes in each period. The last part is conclusion.