How to encover the phenomenon of housing price with urban models
In today’s society, cities with a very high degree of urbanization often have frightening housing prices, which has become a social problem. As a heterogeneous commodity, what are the laws of urban system behind the change of the price of lands or housing areas of a city？ It is a valuable research direction to analyze and summarize these hidden laws through multiple urban models. We can conduct research in three directions and conduct relevant tests.
First of all, as a type of land use, the price of residential land will obviously be affected by the land use of the whole city. Through the use of the land-use model, under the ideal conditions, we can discuss the entire urbanization process of housing prices, as well as the interaction between residential land and other types of land. In the process of urbanization, central business district will appear because of the improvement of social productivity. In the big rent model, it is obvious that the rent of the house decreases with the distance from the city center, and people always hope that the distance between the house and the city center will become shorter, showing an obvious centralized effect. When the distance is classified into more diverse types, such as driving distance, psychological expectation distance, turning distance, etc., the rent will have more abundant change rules. In the multi center period of urbanization process, more outlying business districts appear in cities around central business district. At this time, the rent-distance curve will have inflection points and fluctuations, which means that the price of lands or housing areas of a city are affected by more complex situations.
Secondly, we discuss the relationship between the price of residential land and other types of land. When we discuss other types of land, such as green land and industrial land, the influence of distance also exists. Take the industrial land as an example. In the Hoyt model, with the industrial development of the city, the house price near the industrial land will be lower than the general level. There are two reasons. The poor workers working in the factory do not have enough money to pay the commuting fees, and the risk of accidents around the factory is high. It is worth noting that when discussing the use of land for transportation facilities, because of the direct relationship with distance, the impact of interaction between transportation system and land use on housing prices is of great research value. Through the study of the interaction-distance curve and related formulas based on the land-use-transport-interaction model, we can find that what is more interesting is the difference of housing prices affected by different traffic use conditions and people’s travel purposes. To sum up, the traffic problem is also a cost problem. For a specific place, the cost people spend on transportation also indirectly affects the demand for houses.
Finally, we need to analyze the law that different decision-makers’ behaviors affect housing prices from a more complex urban system, which requires the research of the agent-based model. In the model, we set different parameters, including the agent’s own parameters and environmental parameters, to simulate the logic of different real estate companies’ land acquisition, as well as the game among companies, governments and buyers. After repeated operation simulation and exclusion, we can determine the final results. With visualization as the carrier, we interpret the differences of results under different parameters, and analyze and summarize the impact of decision-makers’ behavior on housing prices under certain conditions.
Urban models are very effective tools to help us understand relevant problems in urban systems, but there are potential errors in using these models. In the urban model, the artificial determination of spatial scale and statistical boundary will cause artificial loss of results. This is a common MAUP problem. In addition, there may be context problems in the model. There may be deviation between the real situation and the simulation, so it is often necessary to set the surrounding size relationship in the interaction. Therefore, we need to check the model at all times, use and study appropriate scales and data to ensure the authenticity and reliability of the model.
