Stability of Housing Prices in Major US Cities: A Time Series Analysis of S&P/Case-Shiller Housing Price Indices

2008 ◽  
Author(s):  
Achintya Ray ◽  
Indrani Ray
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Housing Prices ◽  
Housing Price ◽  
Price Indices ◽  
Series Analysis ◽  
Us Cities

scholarly journals Ridesharing and Motor Vehicle Crashes in 4 US Cities: An Interrupted Time-Series Analysis

2017 ◽  
Vol 187 (2) ◽  
pp. 224-232 ◽  
Author(s):  
Christopher N Morrison ◽  
Sara F Jacoby ◽  
Beidi Dong ◽  
M Kit Delgado ◽  
Douglas J Wiebe
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Us Cities

scholarly journals Gentrification, mobility patterns and health in Lisbon: a time series’ analysis from 2008 to 2018

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
R Sá ◽  
E Soares dos Santos ◽  
T Gabriel ◽  
A Moreira ◽  
P Giraldo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Low Income ◽  
Housing Prices ◽  
Geographical Area ◽  
Mobility Patterns ◽  
Ecological Data ◽  
Series Analysis ◽  
Causal Pathways ◽  
The City

Abstract Background Mobility patterns have a great impact on health. The use of cars is known to be related with increasing air pollution, noise and accidents, and less active transportation, leading to cardiovascular, oncological or respiratory diseases, among others. Gentrification is a process through which the rising value of a geographical area displaces low-income inhabitants, mostly due to rising rents, mortgages and property taxes. This change has the potential for relocating long-time residents and businesses. The aim of this study was to quantify the effect of gentrification in the car influx in the city of Lisbon. Methods A time series' analysis was performed using public ecological data, from 2008 to 2018, of habitation costs per square meter (as a proxy of gentrification) and the number of cars that entered Lisbon through accessing highways. The model was adjusted for confounding factors such as Lisbon's gross income and fuel prices. Results We verified the effect of seasonality in the car influx, with peaks before and after summer - july and october - and a downward trend until 2013 that then inflected and started an upward trend from 2014 to 2018. Habitation costs were positively correlated with car influx into the city (R2=0.773; p < 0.001). In the model, 1€/m2 of increment in housing prices corresponded to 200 more cars that entered the city. Conclusions In Lisbon, gentrification was associated with the increasing number of cars entering the city. These findings may have implications in future policies that regulate housing and mobility. Further research is needed to fully understand the causal pathways of this phenomenon. Key messages Mobility patterns have a great influence on health, and gentrification may influence them. The increase of 1€ per square meter in housing prices lead to an increase of the influx of cars of 200.

scholarly journals EXPLORING THE IMPACT OF REAL ESTATE POLICY ON REAL ESTATE TRADING USING THE TIME SERIES ANALYSIS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1281-1287
Author(s):  
S. W. Shao ◽  
X. Huang ◽  
L. X. Xiao ◽  
H. Liu
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Time Series Analysis ◽  
Housing Prices ◽  
The Real ◽  
The Real Estate ◽  
Series Analysis ◽  
Registration Data ◽  
The People ◽  
The Impact

Abstract. Housing price is a major issue affecting people's lives, but also closely related to the interests of the people themselves. Housing prices are affected by various factors, such as economic factors, population size factors, social factors, national policy factors, the internal factors of real estate and environmental factors. With the deepening of urbanization and the agglomeration of urban population in China, housing prices have been further accelerated. The Chinese government has also introduced a series of policies to limit real estate transactions and affect property prices. This paper also aims to explore a time series analysis method to analyse the impact of real estate policies on real estate prices. Firstly, the article searches for policy factors related to real estate through government official channels such as state, Prefecture and city, and analyses key words related to policy by means of natural language processing. Then, the real estate registration volume, transaction volume and transaction house price data which are arranged into time series are modelled using ARIMA time series model, and the data are processed according to scatter plot, autocorrelation function and partial autocorrelation function graph of the model to identify its stationarity. Finally, the LPPL (logarithmic periodic power) model and MPGA (multi-population genetic algorithm) are used to fit and detect turning points of real estate registration data, and the time series detection algorithm is used to obtain the inflection time nodes of the sequence, and then the relationship between real estate policy and real estate transactions is analysed. Taking the real estate registration data in Wuhan as an example, this paper validates the above time series analysis method. The results show that some real estate policies (such as purchase restriction policy, public rental policy, etc.) have a certain impact on real estate transactions in a short time. Part of the real estate policy (such as graduate security, settlement policy, etc.) does not have a significant impact on real estate transactions. To sum up, the government's brutal blockade of macro-control of the housing market cannot fundamentally solve the housing difficulties of the people, but also standardize the real estate market trading mechanism, innovate the market trading mode, so as to promote the long-term development of the housing market.

Sign in / Sign up

Export Citation Format

Share Document