Does the High-Level Talent Introduction Policy Promote the Rise of Urban Housing Price? Evidence from 11 Cities in China

2021 ◽  
pp. 562-576
Author(s):  
Feng Peng ◽  
Jingran Liu ◽  
Yihao Tian
Keyword(s):  
Housing Price ◽  
Urban Housing ◽  
High Level

Housing price and enterprise financing: does mortgage effect exist?

2019 ◽  
Vol 9 (1) ◽  
pp. 137-152 ◽  
Author(s):  
Shujing Li ◽  
Nan Gao
Keyword(s):  
Financial Development ◽  
Design Methodology ◽  
Empirical Test ◽  
Housing Prices ◽  
Housing Price ◽  
Fixed Effect Model ◽  
Content Type ◽  
Empirical Results ◽  
Effect Model ◽  
High Level

Purpose The purpose of this paper is to explore the influence of the rise in housing prices on enterprise financing and also the sustainability and heterogeneity of this effect. Design/methodology/approach Empirical test, panel data, fixed-effect model, IV and 2SLS were used in this paper. Findings The empirical results indicate that the mortgage effect does exist, and the authors further analyze the heterogeneity of this effect by dividing the sample based on the degree of financial development and property rights; the empirical results reveal that the mortgage effect is significantly higher in places with the high level of financial development. Besides, compared to the SOE enterprise, the mortgage effect has more influence on non-SOE companies. Research limitations/implications The results indicate that the mortgage effect should be considered when regulating housing market, and in order to improve the financing capability of company, its profitability and financial market efficiency should be emphasized. Originality/value This paper not only confirms the existence of the mortgage effect, but also explores its sustainability and heterogeneity, which reveals the risk and bubble in the effect of house market on enterprise financing, and enlightens how to promote financing ability of company.

Local Land-Development Policies and Urban Housing Values

1981 ◽  
Vol 13 (10) ◽  
pp. 1253-1276 ◽  
Author(s):  
Jennifer R Wolch ◽  
S A Gabriel
Keyword(s):  
Land Use ◽  
San Francisco ◽  
Social Relations ◽  
Development Policy ◽  
Local Governments ◽  
Housing Price ◽  
Land Development ◽  
Urban Housing ◽  
Local State ◽  
Development Policies

This paper evaluates the urban housing price impacts of local state land-development policy, and suggests that local governments have multiple objectives and constraints which shape their policy stance toward growth. Based on analyses of San Francisco Bay Area suburban cities, we find that land-use policies of the local state have important housing price effects, with restrictive policies increasing average home values by approximately 15%. Findings on local motivations for use of land-use controls suggest that such policies are adopted to protect private and public consumption levels of residents and thus insure reproduction of social relations. However, constraints on development policy, deriving from historical conditions of urbanization and configuration of the local economic base; development policies of neighboring jurisdictions; and past development policies in a locality, are important factors shaping land-development policy of the local state.

scholarly journals School Desegregation and Urban Change: Evidence from City Boundaries

2012 ◽  
Vol 4 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Leah Platt Boustan
Keyword(s):  
Public Schools ◽  
Metropolitan Areas ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Housing ◽  
Urban Residents ◽  
Urban Residence ◽  
Tax Base ◽  
Central Cities ◽  
The City

I examine changes in the city-suburban housing price gap in metropolitan areas with and without court-ordered desegregation plans over the 1970s, narrowing my comparison to housing units on opposite sides of district boundaries. Desegregation of public schools in central cities reduced the demand for urban residence, leading urban housing prices and rents to decline by 6 percent relative to neighboring suburbs. Aversion to integration was due both to changes in peer composition and to student reassignment to nonneighborhood schools. The associated reduction in the urban tax base imposed a fiscal externality on remaining urban residents. (JEL H75, I21, I28, J15, R23, R31)

Study on Commodity Residential House Price Volatility of the Guanzhong Urban Agglomeration Using Spatial Econometric Analysis

2014 ◽  
Vol 638-640 ◽  
pp. 2436-2441
Author(s):  
Feng Lan ◽  
Ying Tian
Keyword(s):  
Housing Prices ◽  
House Price ◽  
Great Influence ◽  
Housing Price ◽  
Price Volatility ◽  
Urban Housing ◽  
Urban Agglomeration ◽  
Spatial Econometric ◽  
Residential House ◽  
House Price Volatility

This paper was based on the theory of spatial econometric model. It selected the panel datas of the guanzhong urban agglomeration of five core cities from 1998 to 2012, and inspected the commodity residential house price if there is a space dependency relationship between the two cities. On the basis to analyze the main factors influencing the commodity residential house price volatility and research on the influence of the housing price direction. Results show that the sample is significant spatial correlation between urban housing prices. Xi 'an have great influence on regional cities housing price. The urban population, household disposable income, land acquisition costs, sales area are the main influence factors affecting housing price volatility.

scholarly journals Ontological Empowerment: Sustainability via Ownership

2013 ◽  
Vol 23 (1) ◽  
pp. 47
Author(s):  
Jane Greenberg ◽  
Angela Murillo ◽  
John A Kunze
Keyword(s):  
Level Set ◽  
Home Ownership ◽  
Urban Housing ◽  
Scientific Data ◽  
Community Identity ◽  
Functional Requirements ◽  
Ontological Dependence ◽  
High Level ◽  
Ontology Metadata

<p>Positive impacts associated with urban housing/home ownership programs motivate us to study this topic in relation to ontologies. This paper reviews ontological dependence and presents early work underway in the DataONE Preservation and Metadata Working Group (PAMWG) to collectively leverage existing metadata schemes and ontologies. The paper introduces a high-level set of functional requirements and the stackoverflow model that may be used detect highly rated metadata or ontological properties to from a loose cannon for describing scientific data. The long term goal is to establish community identity and rhythm supporting a sustainable ontology/metadata driven workflow.</p>

scholarly journals Temporal and Spatial Effects of Urban Center on Housing Price — A Case Study on Hangzhou, China

2018 ◽  
Vol 5 (1) ◽  
pp. 89 ◽  
Author(s):  
Luhong Chu ◽  
Haizhen Wen
Keyword(s):  
Rapid Development ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Land ◽  
Urban Housing ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Urban Center ◽  
Center City ◽  
The Impact

<em>With the acceleration of urbanization and the rapid development of real estate, people pay more and more attention to the change of urban housing prices. Over time, the change of city center will inevitably affect the urban land or housing prices, which is reflected in the spatial distribution of urban land or housing prices. Therefore, this article attempts to explore the impact of urban center on housing prices from the perspective of multi-center city and study separately from two aspects of time and space. This paper takes the six main urban districts of Hangzhou as the research scope. At the time level, we select the residential data from 2007 to 2015 to construct models respectively based on the hedonic price theory and find that the influence of different urban center on housing price shows a certain change with time. On the spatial level, this paper choses the residential data in 2012 to construct geographic weighted regression model and the result shows that the impact of three centers on housing prices shows a certain degree of spatial heterogeneity.</em>

Forecasting house prices in Iran using GMDH

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Behrooz Nazemi ◽  
Mohsen Rafiean
Keyword(s):  
Predictive Model ◽  
House Prices ◽  
Housing Price ◽  
Urban Housing ◽  
Data Availability ◽  
Group Method ◽  
Housing Crisis ◽  
Affecting Factors ◽  
Current Position ◽  
Content Type

Purpose An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH). Design/methodology/approach This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap. Findings Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom. Research limitations/implications In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors. Originality/value The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.

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