Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

2021 ◽  
Author(s):  
Olgun Aydin ◽  
Krystian Zielinski
Keyword(s):  
Multidimensional Data ◽  
Residential Property ◽  
Self Organizing Map ◽  
Property Market ◽  
Residential Properties ◽  
Price Trends ◽  
Residential Market ◽  
Key Aspects ◽  
The Impact ◽  
Self Organizing

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

scholarly journals BRE INDEX FOR THE HONG KONG RESIDENTIAL PROPERTY MARKET

2004 ◽  
Vol 8 (2) ◽  
pp. 105-119 ◽  
Author(s):  
Eddie Chi Man Hui ◽  
Joe Tak Yun Wong
Keyword(s):  
Hong Kong ◽  
Housing Price ◽  
Capital Gains ◽  
Residential Property ◽  
Property Market ◽  
Price Expectations ◽  
Significant Difference ◽  
Market Outlook ◽  
Price Trends ◽  
Residential Market

This paper examines housing price trends and prediction, of homeowners and potential home buyers, and establishes an independent index (the BRE Index) based on longitudinal telephone surveys collected. The Index, first of this kind in Hong Kong, measures price expectations and benchmarks the level of housing actors’ confidence in the residential market. This is the first paper delivered as part of a government‐funded research project. It synthesizes the key findings of the first survey mounted from 17th to 20th December, 2003. The results show that confidence among housing actors has begun to grow since the property crash in late 1997 with the “overall” BRE Index standing at 564 (0–1000 range). In general, homeowners, people with higher educational level and higher income are optimistic about the market outlook. Residential property prices are expected to rise marginally in the short term. Statistically, there is no significant difference in housing price expectations between homeowners and non‐owners. In their minds, economic condition is the most important factor affecting housing decisions. Apparently, the rising trends in the immediate past have been used to form expectations. The strength of the association between actual capital gains and forecast capital gains is moderately strong, and there appears co‐movement between them. This leads us to believe that hope‐led expectations increase the likelihood of sustaining price increases. The current market is largely driven by expectations. If households formed their expectations in a similar manner in other periods, there would be similar “positive hit” results, which might render the Index more powerful.

scholarly journals Residential Property Behavior Forecasting in the Metropolitan City of Milan: Socio-Economic Characteristics as Drivers of Residential Market Value Trends

2021 ◽  
Vol 13 (7) ◽  
pp. 3612
Author(s):  
Marzia Morena ◽  
Genny Cia ◽  
Liala Baiardi ◽  
Juan Sebastián Rodríguez Rojas
Keyword(s):  
Hedonic Model ◽  
Hedonic Price ◽  
Analysis Model ◽  
Property Market ◽  
Property Development ◽  
Price Performance ◽  
Real Estate Investments ◽  
Residential Properties ◽  
Asset Class ◽  
Residential Market

The phenomenon of urbanization of cities has been the subject of numerous studies and evaluation protocols proposing to analyze the degree of economic and social sustainability of development projects. Through careful research and synthesis of the theoretical framework regarding residential properties’ performance measurement and forecasting, this paper goes deeper into the proposition of property development as an asset class that represents the biggest share of the Italian property market and yet is avoided by the big portfolios. The analysis model was applied to the city of Milan and its Metropolitan Area. The method is based on the development of correlation indices to evaluate different behaviors, through time and a Geographic Information System (GIS) based on the Hedonic Price Method (HPM). Results from a hedonic model estimated for several recent years suggest that, depending on the particular view, the relation between the rent/price performance and the different external and intrinsic variables can represent a useful parameter for evaluating the feasibility of different real estate investments.

scholarly journals On Selecting Approaches to Analysis of the Residential Property Market

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Natalya Kovalevskaya ◽  
Vladislav Tyunkov
Keyword(s):  
Real Estate ◽  
Residential Property ◽  
Property Market ◽  
Dual Nature ◽  
Factors Affecting ◽  
Market Participants ◽  
Consumer Interests ◽  
Private Investors ◽  
The Impact ◽  
Private Individual

The article examines the issues of developing the residential property market taking into account the specifics of real estate as an object of the economic analysis. It reveals the terms of implementing economic interest in investing in residential property, identifies the features inherent in the residential property market as investment and commodity markets. It analyses the dual nature of real estate which explains the development of investment and consumer interests of the residential property market participants. The article analyses the interrelation of «saving - investment - consumption» at the level of implementing private (individual) interests of economic subject. It makes a comparison of various investment assets in terms of their attractiveness for private investors, depending on various factors affecting the decision to invest. It analyses the terms that allow to fully disclose the investment or consumer aspects of the residential property market. It considers the impact of the governmental investment policy directed at supporting and promoting development aspects of the residential property market.

Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership

2011 ◽  
pp. 142-157
Author(s):  
Melody Y. Kiang ◽  
Dorothy M. Fisher ◽  
Michael Y. Hu ◽  
Robert T. Chi
Keyword(s):  
Input Data ◽  
Self Organizing Map ◽  
Market Segment ◽  
Extended Version ◽  
Extended Self ◽  
Data Set ◽  
Step Procedure ◽  
Som Network ◽  
Residential Market ◽  
Self Organizing

This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The Kohonen’s SOM network is an unsupervised learning neural network that maps n-dimensional input data to a lower dimensional (usually one- or two-dimensional) output map while maintaining the original topological relations. We apply an extended version of SOM networks that further groups the nodes on the output map into a user-specified number of clusters to a residential market data set from AT&T. Specifically, the extended SOM is used to group survey respondents using their attitudes towards modes of communication. We then compare the extended SOM network solutions with a two-step procedure that uses the factor scores from factor analysis as inputs to K-means cluster analysis. Results using AT&T data indicate that the extended SOM network performs better than the two-step procedure.

Residential property market determinants: evidence from the 2018 Australian market downturn

2019 ◽  
Vol 38 (2) ◽  
pp. 157-175
Author(s):  
Peng Yew Wong ◽  
Woon-Weng Wong ◽  
Kwabena Mintah
Keyword(s):  
Housing Market ◽  
Economic Conditions ◽  
Residential Property ◽  
Investment Fund ◽  
Property Market ◽  
Data Set ◽  
Content Type ◽  
Econometric Methods ◽  
Residential Market ◽  
Australian Bureau Of Statistics

Purpose The purpose of this paper is to validate and uncover the key determinants revolving around the Australian residential market downturn towards the 2020s. Design/methodology/approach Applying well-established time series econometric methods over a decade of data set provided by Australian Bureau of Statistics, Reserve Bank of Australia and Real Capital Analytics, the significant and emerging drivers impacting the Australian residential property market performance are explored. Findings Besides changes in the significant levels of some key traditional market drivers, housing market capital liquidity and cross-border investment fund were found to significantly impact the Australian residential property market between 2017 and 2019. The presence of some major positive economic conditions such as low interest rate, sustainable employment and population growth was perceived inadequate to uplift the Australian residential property market. The Australian housing market has performed negatively during this period mainly due to diminishing capital liquidity, excess housing supplies and retreating foreign investors. Practical implications A better understanding of the leading and emerging determinants of the residential property market will assist the policy makers to make sound decisions and effective policy changes based on the latest development in the Australian housing market. The results also provide a meaningful path for future property investments and investigations that explore country-specific effects through a comparative analysis. Originality/value The housing market determinants examined in this study revolve around the wider economic conditions in Australia that are not new. However, the coalesce analysis on the statistical results and the current housing market trends revealed some distinguishing characteristics and developments towards the 2020s Australian residential property market downturn.

Impacts of Indoor Environmental Quality (IEQ) Elements on Residential Property Market: A Review

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Asmma’ Che Kasim ◽  
Megat Mohd Ghazali Megat Abdul Rahman ◽  
Maryanti Mohd Raid
Keyword(s):  
Environmental Quality ◽  
Market Price ◽  
Residential Building ◽  
Green Building ◽  
Real Estate Market ◽  
Residential Property ◽  
Indoor Environmental Quality ◽  
Property Market ◽  
Property Price ◽  
The Impact

Indoor environmental quality (IEQ) is among six criteria of Green Building Index (GBI) that need to be achieved by building owner in order to recognize their building as ‘green’ in Malaysia. The benefit of IEQ is to create conducive environment for human health. Besides influenced their overall image, leasing and resale value of the buildings, does indoor environmental quality (IEQ) features will give impact on real estate market in terms of price and rental particularly for residential building property? Therefore, this paper will review the broad literature regarding the impacts of indoor environmental quality (IEQ) for residential building property and its implication to towards property price and rental. The early hypothesis of this paper anticipates that indoor environmental quality (IEQ) features will indirectly increase residential property market price and rental. From this paper, it is hope that the positive impacts of these features will encourage building owners, developers and other main development actors to put these criteria into the same consideration as other criteria in GBI as one of the way to compensate the impact of the building towards economic, environment and social.

Statistical analysis of rainfall event features using the Self Organizing Map with application to Northern Tunisia

2021 ◽  
Author(s):  
Sabrine Derouiche ◽  
Cécile Mallet ◽  
Zoubeida Bargaoui ◽  
Abdelwahab Hannachi
Keyword(s):  
Learning Algorithm ◽  
Global Climate ◽  
Winter Precipitation ◽  
Rainfall Event ◽  
Multidimensional Data ◽  
Rain Event ◽  
Self Organizing Map ◽  
Number Of Clusters ◽  
Northern Tunisia ◽  
Self Organizing

<p>The use of artificial neural networks in problems related to water resources, hydrology and meteorology has received steadily increasing interest over the last decade or so. In this study, the methodology proposed to analyse rainfall features and to investigate the relationships with global climate change is based on  the use of Self-Organizing Map (SOM) and presents a generic character.</p><p>As a first step, daily winter precipitation of northern Tunisia, collected between 1960-2009 over 70 rain gauge stations, are transformed into separate events. This separation is based on the determination of the minimun inter-event time (dry interval) between two independent and consecutive rain events. Six rainfall event features (i.e., average rain event accululation, average event duration, seasonnal accumulation, number of rainy day…) are thus extracted for each of the (70 stations x 50 winter seasons).</p><p>In the second step, SOM is applied to analyse the six rainfall features. The SOM is an unsupervised learning algorithm, used as a technique vector quantization, allowing the modeling of probability density functions. It divides the set of multidimensional data (vectors of six features in our case) into clusters. As in k-means, rainfall stations and years with similar characteristics are grouped in a cluster represented by its centroid point named referent. SOM enables moreover the projection of high-dimensional data onto a low dimensional (usually two-dimensional) discrete lattice of neurons as an output layer (map space). The structure of the neurons in the map and the cost function used for its training, ensure that neighboring neurons in the map space are associated with neighboring referents in the initial space. This conservation of the topology allows the analysis of multidimensional nonlinear relationships between the six selected descriptors by visualizing their projection in the map space.</p><p>For a better representation of the input dataset a 16×20 neurons map is used. But a such number may complicate the synthesis of some spatial or temporal specificities. So, this large number of neurons is aggregated into a smaller number of clusters. For that an hierarchical agglomerative clustering (HAC)  is applied in the third step. This hierachical process is initiated by accepting each neuron as a separate cluster. Then, at each stage of the algorithm, similar clusters, using Ward distance, are combined in pairs.</p><p>The fourth step allows to determine the final number of clusters by using visually-based method known as data image. This consists of mapping the dissimilarity matrix of the referents into an image framework where each pixel reflects the magnitude of each value. Here rows and columns can be reordered based on hierarchical clustering of the referents The blocs observed along the diagonal of each image represents the clusters.</p><p>Finaly the northern Tunisia winter precipitation are classified into four rainfall situations from the driest to the wettest while also taking into account the rainfall day frequency during the season and rainfall event types. The projection of external climatic variables on the map will make it possible to analyse the links between the four observed rain regimes and the global climate.</p>

The impact of large-scale atmospheric patterns on Heatwaves in summer Northern China based on Self-Organizing Map

2020 ◽  
Author(s):  
Chen Shi ◽  
Wang Kaicun ◽  
Zhou Chunlüe
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Stepwise Regression ◽  
Northern China ◽  
Subtropical High ◽  
Self Organizing Map ◽  
Okhotsk Sea ◽  
Circulation Patterns ◽  
The Impact ◽  
Self Organizing

<p>Heatwave is affected by large-scale atmospheric circulation on temperature-related climates in the context of global warming. Recently Northern China have experienced an increase in heatwaves which is partly due to the atmospheric circulation. This study aims to address the influence clearly. Northern China heatwaves are computed on excess hot factor (EHF) and the five EHF indexes are studied afterwards to get a picture of heatwaves in summer Northern China. China circulation patterns are classified into nine typical circulation patterns on self-organizing map (SOM) which then can be described quantitatively by pattern factors: frequency, persistence and maximum persistence. Pearson correlation analysis and stepwise regression analysis are applied for exploring the impact. Results show the spatial pattern of the times of individual heatwave event (HWN) and the days of the longest heatwave duration (HWD) are high value everywhere in Northern China. The overall EHF indexes all rising in time series (P<0.05) and the regional heatwave occurrence have trends of 0.79 day per year (P<0.05). However, the factors of the patterns show inconspicuous tendency. Two patterns with significant correlations (P<0.05) are proved to be suggestive of Okhotsk Sea high and West Pacific Subtropical High. It declares that the Okhotsk Sea high favors Northern China heatwave occurrence rather than subtropical high: the warm center over Okhotsk Sea transfer heat upper and west, generating the high temperature and persist high pressure system, causing heatwave happening in summer Northern China. The two related atmospheric circulation patterns explain 38% of the heatwave occurrence based on stepwise regression model, the Okhotsk Sea high gets the coefficient of 0.443 and the subtropical high is -0.347.  </p>

Price effects of surface street traffic on residential property

2014 ◽  
Vol 7 (2) ◽  
pp. 189-203 ◽  
Author(s):  
James E. Larsen ◽  
John P. Blair
Keyword(s):  
Residential Property ◽  
Single Family ◽  
Content Type ◽  
Arterial Streets ◽  
Price Effects ◽  
Residential Properties ◽  
Negative Effect ◽  
Street Traffic ◽  
The Impact ◽  
Family House

Purpose – The purpose of this study is to gauge and compare the impact of surface street traffic externalities on residential properties. Limited previous research indicates that negative externalities dominate for single-family houses. Our objective is to verify that this result applies to our sample, and to determine if the same result extends to multi-unit rental properties. Design/methodology/approach – Hedonic regression is used to analyze data from 9,680 single-family house transactions and 455 multi-unit rental properties to measure the influence of surface street traffic on the price of the two property types. Findings – Houses located adjacent to an arterial street sold at a 7.8 per cent discount, on average, compared to similar houses located on collector streets. Limiting the analysis to houses adjacent to an arterial street (where traffic counts were available), price and traffic count are negatively related. The results for multi-unit rental dwellings are dramatically different. Multi-unit properties adjacent to an arterial street sold at a 13.75 per cent premium compared to similar properties on collector streets, and when limiting the analysis to properties on arterial streets, no significant relationship was detected between price and traffic volume. Originality/value – This is the first empirical study of the influence of surface street traffic on both single-family houses and multi-unit rental residential property. Evidence is provided that traffic externalities impact the two types of properties quite differently. To the extent that this result applies to other locations, the authors suggest planners may be able to use such information to reduce the negative effect of traffic externalities on residential property associated with changes that will increase traffic flow.

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