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Analysis Of Influencing Factors And Spatial Differences In Housing Rental Rate Of Return

Posted on:2024-08-29Degree:MasterType:Thesis
Country:ChinaCandidate:J L HuangFull Text:PDF
GTID:2530306938952769Subject:Finance
Abstract/Summary:PDF Full Text Request
With the deepening of China’s new urbanization process,new citizens and youth groups in hot cities are facing comparatively severe housing pressure.The Party Central Committee and the State Council have explicitly requested to speed up the establishment of housing system with multi-body supply,multi-channel guarantee,encouraging both housing purchase and renting,and have emphasized the cultivation and development of the housing rental market to promote the stable and healthy development of the real estate market.In this context,rental income is the main source of income for housing renting and the housing rental return is the key indicator in various financial support models and paths for housing stock rental.Therefore,this thesis focuses on the rental return of housing stock,and conducts an in-depth quantitative analysis by combining the building characteristics,community characteristics and location characteristics of the underlying stock of housing assets.With the help of micro data on housing rental and purchase transactions in Beijing’s stock housing market,we quantify the basic characteristics,influencing factors and spatial heterogeneity of housing rental returns based on theories of residential demand and residents’ site-selection behaviors and empirical analysis ideas from characteristic price models.The empirical findings of this thesis can provide a more scientific reference basis for the assessment of the return risk of financial support to the stock housing rental market and related investment decisions,thus helping to realize a risk-controlled and commercially sustainable housing rental market.First of all,this thesis finds from a review of academic literature and typical experiences at home and abroad that,in the process of financial support for the development of housing stock rental market,all kinds of financial instruments,models and paths need to focus on the core rental return index.At the same time,investors may lack comprehensive consideration of the necessary characteristics of the underlying housing assets when selecting investment targets,thus under-evaluating the investment risks and ultimately leading to damage to their interests.The critical problem is that investors have not yet effectively conducted a refined assessment and quantitative analysis of the investment return on underlying housing assets.However,the current empirical studies on financial support for the housing stock rental market generally focus on case studies and empirical references of the overall model path,while there are few empirical studies on the quantitative analysis of rental rate of return based on the characteristics of the underlying housing assets.Therefore,relying on the advantages of rich housing types,extensive spatial distribution and detailed characteristics of Beijing’s inventory housing micro-transaction data,this paper uses characteristic price models and geographically weighted regression models to accurately assess the rental returns of underlying stock of housing assets,as well as the heterogeneity of rental returns under different building characteristics,community characteristics and location characteristics.Secondly,here are the main findings of this thesis’s empirical study:(1)the age,number of bedrooms and housing area in building characteristics;the quality of property services in the neighborhood where the housing is located in community characteristics;and the most significant contribution to the rental return is the supporting services and facilities such as educational and medical resources around the neighborhood where the housing is located in location characteristics.(2)Further analyzing the heterogeneity of rental returns of the housing stock with different architectural,community and location characteristics,we find that the rental returns of older houses depend more on their community and location characteristics,while the rental returns of younger houses,or newer houses,are more influenced by their own architectural characteristics.(3)Considering the potential endogeneity caused by spatial autocorrelation,this thesis further analyzes the spatial heterogeneity of the factors influencing housing rental returns using a geographically weighted regression model to quantify the specific differences in housing rental returns across different spatial locations due to factors such as housing size,number of bedrooms,and distance of the housing location from the city center.Finally,the empirical findings of this thesis quantitatively identify the factors influencing the rental returns of underlying housing assets and their spatial distribution patterns,and the main results have certain implications for both public and private sector decisions related to financial support for housing rental.First,for the public sector,the assessment and prediction of rental return rates of existing housing projects or potential rental housing projects can identify critical issues and specific working tools to achieve more effective housing rental supply and thus improve the efficiency of solving the housing problems of new citizens;Second,for various investors,the comprehensive assessment and analysis of the architectural,community and location characteristics of the underlying stock of housing assets can screen out high-quality investment targets with more stable rental rate of return,effectively control potential financial risks during the acquisition process of the underlying stock of housing assets,and help promote financial support for housing stock rental projects to achieve sustainable profitability;Third,for housing rental operators,accurate identification of the factors influencing the housing rental return and the extent of their contribution,and targeted construction and renovation of long-term housing rental or revitalization of housing stock can promote their ability to improve the supply and operation of housing rental.
Keywords/Search Tags:Financial support, Housing rental, Housing stock, Spatial heterogeneity, Investment decision
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