Stickiness of rental rate and housing vacancy rate

2020 ◽  
Vol 195 ◽  
pp. 109487
Author(s):  
Haoyu Wang
Keyword(s):  
Vacancy Rate ◽  
Rental Rate ◽  
Housing Vacancy

scholarly journals Predicting Detached Housing Vacancy: A Multilevel Analysis

2022 ◽  
Vol 14 (2) ◽  
pp. 922
Author(s):  
Jaekyung Lee ◽  
Galen Newman ◽  
Changyeon Lee
Keyword(s):  
Multilevel Model ◽  
Local Environment ◽  
Critical Issue ◽  
Building Structure ◽  
Model Design ◽  
Vacancy Rate ◽  
City Center ◽  
Individual Housing ◽  
The Individual ◽  
Housing Vacancy

Urban shrinkage is a critical issue in local small- and medium-sized cities in Korea. While there have been several studies to analyze the causes and consequences of vacancy increases, most have only focused on socioeconomic associations at larger scale and failed to consider individual housing level characteristics, primarily due to a lack of appropriate data. Based on data including 52,400 individual parcels, this study analyzes the primary contributors to vacant properties and their spatial distribution through a multilevel model design based on data for each parcel. Then, we identify areas at high risk of vacancy in the future to provide evidence to establish policies for improving the local environment. Results indicate that construction year, building structure, and road access conditions have a significant effect on vacant properties at the individual parcel level, and the presence of schools and hypermarket within 500 m are found to decrease vacant properties. Further, prediction outcomes show that the aged city center and areas with strict regulations on land use are expected to have a higher vacancy rate. These findings are used to provide a set of data-based revitalization strategies through the development of a vacancy prediction model.

scholarly journals International Real Estate Review

2018 ◽  
Vol 21 (3) ◽  
pp. 397-418
Author(s):  
Chin-Oh Chang ◽  
◽  
Shu-Mei Chen ◽  
Keyword(s):  
Housing Market ◽  
Home Ownership ◽  
Housing Prices ◽  
Housing Price ◽  
Poor Quality ◽  
Housing Demand ◽  
Vacancy Rate ◽  
Income Ratio ◽  
Holding Cost ◽  
Housing Vacancy

This paper discusses the contradicting phenomenon of housing demand in Taiwan. First, an introduction is given on the three primary characteristics of the housing market in Taiwan, which are a high housing vacancy rate, high housing prices and high home ownership. Secondly, we explore the motivation and preferences behind housing purchase. Since the housing price-income ratio continues to increase, unaffordable housing prices cause households to suffer from poor quality of life. The issues of housing justice are highlighted. Recently, the demographics and social values have rapidly changed. Therefore, even if homebuyers face unaffordable housing prices, they still prefer to buy housing instead of renting due to the traditional cultural belief that ¡§to have land is to have wealth¡¨. This has resulted in the phenomenon with high home ownership rate yet high housing prices. On the other hand, the low holding cost of housing and imbalance in urban and rural development perpetuate the high housing vacancy rate in the housing market. This results in an unhealthy housing market and misallocation of resources. Finally, recommendations for related government policy making are made based on the findings.

scholarly journals ESTIMATING HOUSING VACANCY RATE IN QINGDAO CITY WITH NPP-VIIRS NIGHTTIME LIGHT AND GEOGRAPHICAL NATIONAL CONDITIONS MONITORING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1319-1326 ◽  
Author(s):  
X. Niu
Keyword(s):  
Population Distribution ◽  
Monitoring Data ◽  
Basic Unit ◽  
Distribution Data ◽  
Vacancy Rate ◽  
Rapid Urbanization ◽  
Nighttime Light ◽  
Qingdao City ◽  
The City ◽  
Housing Vacancy

Accompanying China's rapid urbanization in recent decades, especially in the new millennium, the housing problem has become one of the most important issues. The estimation and analysis of housing vacancy rate (HVR) can assist decision-making in solving this puzzle. It is particularly significant to government departments. This paper proposed a practical model for estimating the HVR in Qingdao city using NPP-VIIRS nighttime light composed data, Geographic National Conditions Monitoring data (GNCMD) and resident population distribution data. The main steps are: Firstly, pre-process the data, and finally forming a series of data sets with 500*500 grid as the basic unit; Secondly, select 400 grids of different types within the city as sample grids for SVM training, and establish a reasonable HVR model; Thirdly, using the model to estimate HVR in Qingdao and employing spatial statistical analysis methods to reveal the spatial differentiation pattern of HVR in this city; Finally test the accuracy of the model with two different methods. The results conclude that HVR in the southeastern coastal area of Qingdao city is relatively low and the low-low clusters distributed in patches. Simultaneously, in other regions it shows the tendency of the low value accumulation in the downtown area and the increasing trend towards the outer suburbs. Meanwhile the suburban and scenery regions by the side of the sea and mountains are likely to be the most vacant part of the city.

scholarly journals Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1920 ◽  
Author(s):  
Mingzhu Du ◽  
Le Wang ◽  
Shengyuan Zou ◽  
Chen Shi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Physical Environment ◽  
High Spatial Resolution ◽  
Geospatial Data ◽  
Vacancy Rate ◽  
Night Time ◽  
Census Tract Level ◽  
Accuracy Increase ◽  
Housing Vacancy

The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities.

scholarly journals International Real Estate Review

2007 ◽  
Vol 10 (1) ◽  
pp. 119-150
Author(s):  
Li-Min Hsueh ◽  
◽  
Hsi-Peng Tseng ◽  
Chang-Chiang Hsieh ◽  
◽  
...  
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
Positive Impact ◽  
Average Rate ◽  
Housing Price ◽  
Vacancy Rate ◽  
Cross Sectional ◽  
Excess Supply ◽  
The Real Estate ◽  
Housing Vacancy

In this research, cross-sectional data for the township level obtained from the 1990 and 2000 Population and Housing Census are used to study the phenomenon of high housing vacancy rates in Taiwan. Three simultaneous equations for housing price, vacancy rate, and moving rate are derived and estimated using 3SLS. The estimation results show that, in 1990, in a booming market situation, both expected housing price and current housing price had a strong, positive impact on the vacancy rate; however, the housing vacancy rate did not display a negative impact on housing price as expected. The results for 2000 show that housing price did not significantly affect the vacancy rate; however, the vacancy rate had a negative impact on housing price that was highly statistically significant. This result reflected the fact that housing market operation had swung to another extreme after the real estate bubble that started in the late 1980s and burst in the mid-1990s. The natural vacancy rate for each township can be obtained from the estimation results. The average rate for 2000 was 0.11 to 0.12, compared to an actual vacancy rate of 0.158, which implied that 75% of townships had an excess supply of housing. Only Taipei City, Kaohsiung City and townships in areas inhabited by Taiwan’s indigenous peoples had, on average, a relatively low excess supply rate.

scholarly journals Housing Vacancy Rate in Major Cities in China: Perspectives from Nighttime Light Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhiru Tan ◽  
Donglan Wei ◽  
Zixu Yin
Keyword(s):  
Data Center ◽  
Housing Prices ◽  
Vacancy Rate ◽  
Nighttime Light Data ◽  
Building Information ◽  
Nighttime Light ◽  
Vacancy Rates ◽  
Second Tier ◽  
Nighttime Light Imagery ◽  
Housing Vacancy

In recent years, the phenomenon of housing vacancy rate (HVR) has attracted considerable attention, especially concerning unjustified expansions of Chinese cities. The aforementioned trend is disadvantageous in that it will ultimately lead to tremendous wastage of valuable land that could otherwise be more productively utilized. Consequently, the methods for accurately determining the HVR are of great importance. Based on nighttime light data from the Luojia 1-01 nighttime light imagery provided by Wuhan University in June 2018 and the building data obtained from the Resources and Environmental Sciences Data Center, we estimated the HVRs of 49 cities in China by determining the building areas and considering the floor height. The results revealed that (1) the lowest (15%) and highest (24.3%) HVRs occur in Shenzhen and Nanning, respectively. (2) The urban HVR correlates positively with the three production structures (0.3143) but is significantly negatively correlated with population (0.3841), GDP (0.6139), and urban average housing prices (0.5083). (3) The first-tier, new first-tier, and second-tier cities showed the lowest (16.9%), relatively concentrated (20.5%), and highest (21.3%) average vacancy rates, respectively. (4) The vacancy rate is relatively low in the eastern coastal areas, whereas high in the northeast and western inland areas. The proposed method can help urban planners by identifying vacant areas and providing building information.

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