• Title/Summary/Keyword: housing price estimation

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ANALYZING THE EFFECT OF THE RESIDENCE AND REAL ESTATE POLICIES ON HOUSING PRICE

  • Jin-Ho Noh;Jae-jun Kim;Sun-Sik Kim;Eun-Jin Ahn;Hye-In Lee;Yoon-Sun Lee
    • International conference on construction engineering and project management
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    • 2007.03a
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    • pp.490-497
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    • 2007
  • Since the foreign currency crisis, Korean economy has suffered recession and the government launches residence and real estate policy in order to increase the demand and trade of real estate and to help the economy revitalization. 1 As a result, the rate of economy growth is shown the high increase with the figure of 10.9% in 1999 and 8.8% in 2000. However, it brings overheating market as a negative effect. Although, the government established the policy for the control of speculation, the policy causes instability of economy. This study is to analyze the effect between the residence policy and the housing cost since the foreign currency crisis through housing sale price estimation and housing lease price estimation and is to apply the basis data of the next residence policy.

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How the Pattern Recognition Ability of Deep Learning Enhances Housing Price Estimation (딥러닝의 패턴 인식능력을 활용한 주택가격 추정)

  • Kim, Jinseok;Kim, Kyung-Min
    • Journal of the Economic Geographical Society of Korea
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    • v.25 no.1
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    • pp.183-201
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    • 2022
  • Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

An Empirical Study on the long-term Relationship between House Prices and Inflation in the U.S. (주택가격과 물가의 장기관련성에 관한 실증연구 : 미국을 중심으로)

  • Lee, Young Soo
    • International Area Studies Review
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    • v.14 no.3
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    • pp.246-263
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    • 2010
  • This study examines how the long-run relations between housing price and inflation in the United Sates have changed since the year of 2000. Johansen co-integration test, estimation of long-run equilibrium equation, and Granger causality tests are conducted, based on the VECM. Data covers the period from the first quarter of 1975 to the second quarter of 2010. I adopt the recursive estimation method in which the final period of the estimation is expanded by one quarter, starting from the first quarter of 2000. The empirical results are as follows: (1) In spite of the sharp increase of housing price, the long-run relationship of house prices and inflation has been remained stable until 2007, showing that house prices are a stable inflation hedge in the long run. (2) The housing price plunge since 1997 does not seem to be related to the restore of the long-run relationship between housing prices and inflation. (3) Granger causality test results support the hypothesis that inflation granger-causes housing prices with 10% significance level, but reject the hypothesis that housing price granger-causes inflation.

A Study on Estimation the Inplicit Price of Housing Characteristics According to Tenure Type and Region (주택 특성에 대한 내재가격 추정에 관한 연구)

  • 제미정
    • Journal of the Korean Home Economics Association
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    • v.28 no.1
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    • pp.57-66
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    • 1990
  • The purpose of this study was to investigate the analytical model of the implicit price according to objective and subjective characteristics of housing. The hedonic price regression was used for estimating the implicit price. The subjectives of this study were 1,143 dwellers who live in Seoul metropolitan area. Taejeon, and Jeonju. Satistical analyses were conducted using frequencies, percentiles, mean, and multiple regression. The major findings were as follows: 1. There was a significant difference in the implict price of the apartment between owners and renters. 2. There was a sginificant difference in the implicit price of the apartment among Seoul metropolitan area, Taejeon, and Jeonju. 3. Using a stepwise multiple regression method, the order of variables as they were entered in the model were different between tenure types (owner/renter), and regions(Seoul metroplitan area/Taejeon/Jeonju). 4. The linear model was the most appropriate noe which explained the housing price. 5. Subjective characteristics of housing in Taejeon and Jeonju had an effect on the housing price more than those in Seoul metropolitan area.

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Robust spectral estimator from M-estimation point of view: application to the Korean housing price index (M-추정에 기반을 둔 로버스트 스펙트럴 추정량: 주택 가격 지수에 대한 응용)

  • Pak, Ro Jin
    • The Korean Journal of Applied Statistics
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    • v.29 no.3
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    • pp.463-470
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    • 2016
  • In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.

An Empirical Study on the Estimation of Housing Sales Price using Spatiotemporal Autoregressive Model (시공간자기회귀(STAR)모형을 이용한 부동산 가격 추정에 관한 연구)

  • Chun, Hae Jung;Park, Heon Soo
    • Korea Real Estate Review
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    • v.24 no.1
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    • pp.7-14
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    • 2014
  • This study, as the temporal and spatial data for the real price apartment in Seoul from January 2006 to June 2013, empirically compared and analyzed the estimation result of apartment price using OLS by hedonic price model for the problem of space-time correlation, temporal autoregressive model (TAR) considering temporal effect, spatial autoregressive model (SAR) spatial effect and spatiotemporal autoregressive model (STAR) spatiotemporal effect. As a result, the adjusted R-square of STAR model was increased by 10% compared that of OLS model while the root mean squares error (RMSE) was decreased by 18%. Considering temporal and spatial effect, it is observed that the estimation of apartment price is more correct than the existing model. As the result of analyzing STAR model, the apartment price is affected as follows; area for apartment(-), years of apartment(-), dummy of low-rise(-), individual heating (-), city gas(-), dummy of reconstruction(+), stairs(+), size of complex(+). The results of other analysis method were the same. When estimating the price of real estate using STAR model, the government officials can improve policy efficiency and make reasonable investment based on the objective information by grasping trend of real estate market accurately.

The Effects of Locational Point Representation of Apartment Complexes on Hedonic Valuation of Air Quality (공동주택 위치표현 방법이 대기질의 한계잠재가격 측정에 미치는 영향)

  • Chul Sohn
    • Journal of the Korean Geographical Society
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    • v.38 no.6
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    • pp.949-960
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    • 2003
  • The marginal implicit price of air quality can be measured by taking a partial derivative of hedonic price function (HPF) with respect to the level of air quality. It has been pointed out that the size of the marginal implicit price varies with the use of different function forms, different estimation methods, and the different ways of measuring air quality level in estimating HPF. In addition to these factors, this study shows theoretically and empirically the way housing properties are represented on a digital map could differentiate the size of marginal implicit price of air quality when GIS is used to measure location attributes of the housing properties in the Korean apartment market. Furthermore, this study shows that the degree of difference in the marginal implicit price due to the manner in which housing properties are represented on a digital map can be larger than the degree of difference in the marginal implicit price due to using different function forms and estimation methods. The major implication from the results of this study is that one should carefully try diverse ways of representing housing properties in the Korean apartment market on a digital map in the process of estimating HPF, as he or she usually tries diverse function forms and estimation methods, to see if the value of the marginal implicit price of air quality varies substantially.

A Basic Study on Estimation Model Development by Applying Probabilistic Forecasting Method for Determining Optimal Price of Residential Officetel (확률론적 추정 기법을 적용한 주거형 오피스텔의 최적 분양가 산정 모델 개발 기초연구)

  • Jang, Jun-Ho;Kim, Tae-Hui;Ha, Sung-Eun;Son, Ki-Young
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2017.11a
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    • pp.191-192
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    • 2017
  • In response to the economic depression, the demand for fixed rent income has increased according to the easing construction regulations. it caused indiscriminated investment to stakeholders. This leads to oversupply in the multi-family Housing market and increases unsold housing and vacancy rates except specific area such as Gangnam-gu.In order to solve this issue, although studies on the optimization price of apartment houses has been conducted, the study is insufficient regarding on residential officetel. Therefore, the objective is to suggest a basic study on optimal price estimation model development by using probabilistic forecasting method in planning phase. To achieve the objective, first, variables are defined such as expenses, financial costs, income, etc. Second, causal loop diagram is suggested. Third, basic optimization prices estimation model is developed. In the future, this study can be used as one of decision making tools in planning phase of officetel development projects.

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A Study on Relationship between House Rental Price and Macroeconomic Variables (주택 전세가격과 거시경제변수간의 관계 연구)

  • Kim, Hyun-Woo;Chin, Kyung-Ho;Lee, Kyo-Sun
    • Korean Journal of Construction Engineering and Management
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    • v.13 no.2
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    • pp.128-136
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    • 2012
  • In this study, we investigated the macroeconomic variables that affect housing prices thus creating a large impact on people's lives as well as the real estate market. For the study, the macroeconomic variables able to influence the House Rental Price (housing price by lease or deposit) were used for an analysis as follows: housing sales price index, household loans rate, total household savings, the number of employees and a multiple regression analysis was performed using a time series for each macroeconomic variable. As a result of the analysis, the House Rental Price was affected by all of four macroeconomic variables. The House Rental Price increased as each variable enlarged. In conclusion, this study may be useful for finding a solution for stabilizing the House Rental Price as well as for the establishment of efficient and sustainable policies for the housing market.

A Study on the Seoul Apartment Jeonse Price after the Global Financial Crisis in 2008 in the Frame of Vecter Auto Regressive Model(VAR) (VAR분석을 활용한 금융위기 이후 서울 아파트 전세가격 변화)

  • Kim, Hyun-woo;Lee, Du-Heon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.9
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    • pp.6315-6324
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    • 2015
  • This study analyses the effects of household finances on rental price of apartment in Seoul which play a major role in real estate policy. We estimate VAR models using time series data. Economy variables such as sales price of apartment in Seoul, consumer price index, hiring rate, real GNI and loan amount of housing mortgage, which relate to household finances and influence the rental price of apartment, are used for estimation. The main findings are as follows. In the short term, the rental price of apartment is impacted by economy variables. Specifically, Relative contributions of variation in rental price of apartment through structural shock of economy variables are most influenced by their own. However, in the long term, household variables are more influential to the rental price of apartment. These results are expected to contribute to establish housing price stabilization policies through understanding the relationship between economy variables and rental price of apartment.