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Generally, housing within the same area tends to share similar structural and locational characteristics as well as being characterized by the same homebuyers’ preferences. This information provides insight on the existence of housing submarkets. Thus, this study aims to identify the existence of housing submarkets in Johor Bahru and Kulai, Malaysia through principal component analysis and cluster analysis. A total of 29 housing attributes were included in the study. The results from the principal component analysis have successfully identified four principal components that represent the basic facility, main infrastructure, less common facility and housing quality characteristics. On the other hand, cluster analysis managed to identify the existence of four housing submarkets in Johor Bahru and Kulai, where three submarkets showed the characteristics of housing quality, whereas one submarket appeared to exhibit the characteristic of main infrastructure. The findings of this study provide valuable information on the existence of four housing submarkets in Johor Bahru and Kulai areas, which will benefit the city governments, real estate developers, mortgage lenders, non-profit organizations, as well as individual homeowners in making critical choices and developing market-appropriate strategies.

The improvement of property price modelling accuracy using property market segmentation approaches is well documented in the housing market. However, that cannot be said of the commercial property market which is adjudged to be volatile, heterogeneous and thinly traded. This study, therefore, determines if the commercial property market in Malaysia is spatially segmented into submarkets and whether accounting for the submarkets improves the accuracy of price modelling. Using a 11,460 shop-offices transaction dataset, the commercial property submarkets are delineated by using submarket binary dummies in the market-wide model and estimating a separate hedonic model for each submarket. The former method improves the model fit and reduces error by 5.6% and 6.5% respectively. The commercial property submarkets are better delineated by estimating a separate hedonic model for each submarket as it improves the model fit by about 7% and reduces models’ error by more than 10%. This study concludes that the Malaysian commercial property market is spatially segmented into submarkets. Modelling the submarkets improves the accuracy and correctness of price modelling.

Currently, the trends in urban public transport have been changing over the years in developing countries for mobilization and accessibility development. Urban public transportation systems are the most popular in Selangor State, including big cities such as the Klang Valley Region. Objective measures of spatial pattern and hotspots have been used to understand how urban public transport development relate to open access. This method relies on specific spatial information and available web-based tool that shows the pattern primarily based on given vicinity and statistics connectivity. To date, several studies have finished tested in developed countries. In this study, we use Geographic Information Systems to analyse and consider hotspots identification precisely and efficaciously. Therefore, in this paper, we focus on two types of point sample evaluations – Gi* hot spot and point density analysis evaluation as statistical operations. Public rail transport was evaluated as a validation to describe the percentage of distribution of open access. The final result, GIS mapping capabilities to show that GIS's technology offers to the variation of urban public transport relate to public services, is to create maps and spatial interpretations.

‘Location, location, location’ is a real property parlance mostly used to describe the influence of location in the property market. Location is mainly considered as the most significant influencer of commercial property prices. Location is modelled traditionally using hedonic pricing model by either proxy location dummies or distances relative to other neighbourhood features. This was shown to be inadequate due to spatial autocorrelation and heterogeneity inherent in spatial data, which jeopardises the estimates' consistency. Consequently, spatial econometrics is used to explicitly model location into property pricing by controlling spatial effects of autocorrelation and heterogeneity. Housing studies dominate the use of this approach with limited application in the commercial property market. This paper reviewed spatial econometrics and found that the commercial property market exhibits significant spatial dependence and heterogeneity. Accounting for such effects improves model accuracy significantly. It, therefore, recommends increase use of spatial econometrics in commercial property market modelling.

Geographic location naturally generates spatial patterns that are either clustered, dispersed, or random. Moreover, Tobler’s First Law of Geography is essentially a testable assumption in the concept where geographic location matters and one method for quantifying Tobler’s law of geography is through measures of spatial autocorrelation. Therefore, the purpose of this study is to identify the spatial patterns of housing distribution in Johor Bahru through the spatial autocorrelation method. The result of the global spatial autocorrelation analysis demonstrates a high degree of clustering within the housing distribution, as well as the identification of a clustered pattern with a highly positive Moran’s I value of 0.995207. Following that, the LISA cluster map successfully identified individual clusters of each housing unit with their neighbours through the red and blue colours displayed on the map, as well as revealing home buyers’ preferences for a property in each location.

Housing is a country’s biggest asset. Hence, the pattern of the housing price index (HPI) is an important topic to gain insight into the housing market while identifying the prevailing housing issues. The determinants of housing price vary for each city and state based on the different characteristics in each location. Accordingly, HPI should consider the property’s quality differences. Besides, national HPI is insufficient and restricted to the housing price at the state level. Thus, the study focused on constructing a specified HPI model for different cities, districts, and states. Effective HPI can give parties a better idea of the current property market situation and act as an analytical tool in managing the sector. Specifically, the study aims to examine the relationship between the heterogeneity housing attributes and housing prices of the terraced properties in Johor Bahru, Malaysia. Additionally, the study provides detailed information on the key determinants of the housing price variation in Johor Bahru. Hedonic price analysis is useful in constructing HPI, expressing housing price as a function of vector property characteristics. Furthermore, HPI is constructed based on the yearly indices and by pooling the data into certain periods. The results show the percentage of variance explained by the factors of HPI for the terraced properties in Johor Bahru. Correspondingly, the underlying correlation between the tested housing attributes with the housing price is explained through the analysis results.

Currently, the most severe infectious disease was the new coronavirus disease (COVID-19) in all countries in 2019 and 2020. At the end of December 2019, in Wuhan, China, there was an international cluster of cases involving Novel Coronavirus pneumonia (SARS-COV-2). The worldwide number of active cases and deaths is rising, especially in the top countries such as the United States (U.S), Brazil, and India. In Malaysia, these cases of COVID-19 have significantly decreased the number of active infections and deaths in May and June 2020. COVID-19 has had a significant effect on human life, socio-economic growth, and public relation. It is aimed at senior groups and individuals with various health conditions such as cancer, respiratory problems, diabetes, hypertension, and heart-related issues. The World Health Organization (WHO) has formally declared COVID-19 as an international emergency case. As a result, Kuala Lumpur was the most affected state in Malaysia as of 12 July 2020, followed by Selangor, Negeri Sembilan, and Johor. Regardless of the infection chain ratio, the favorable cases in each affected state of Malaysia are rising every day. The Malaysian Government attempted to split the infection chain ratio affected by COVID-19 via the lockdown definition. The research aims to use GIS software to analyze COVID-19's spatial trend distribution and temporal pattern analysis for human health. Geographic information systems (GIS) technologies have played a significant role in spatial information, spatial tracking of confirmed cases, active case, death, and discharge cases, and predicting the magnitude of the spread. Monitoring, evaluating, and planning using geospatial analysis are essential for controlling the spread of COVID-19 within the country.

About 75% of urban areas in Malaysia have an excellent public transportation system. Malaysian Intelligent Transport System (MITS) Blueprint 2017 – 2022 is developed and mentioned public transport as the infrastructure for traffic management and the Fourth Industrial Revolution (4IR) will change the global landscape of public transportation. The primary purpose of this paper is to use Geographic Information Systems (GIS) for identifying spatial accessibility to transport in Klang Valley, Selangor supported the movement of the walk-distance analysis techniques. A geospatial database was created that has the coordinate of urban public transport (railway mode), home residence distribution, and road networks. Accessibility index tool and geoprocessing by overlay analyses were selected because of the analysis tools for this paper. The paper results indicate that urban public transport in the Klang Valley districts of Selangor has fewer registered railway line stations. Several areas of Klang Valley region have low accessibility based on z-score either dispersion or clustering pattern to public transport because they fall outside the space-distance service area for every terminal. Local transport planners in Klang Valley may use the created application to allocate public transport in low access areas.

Everything that has a location in geographic space will naturally generate a spatial pattern either clustered, dispersed or random. A testable assumption in the concept where geographic location matters is Tobler’s First Law of Geography whereas one means for quantifying the law of geography by Tobler is through the measures of spatial autocorrelation. Hence, this study aims to identify the spatial clustering patterns of housing distribution in Johor Bahru by applying spatial autocorrelation methods. Through global spatial autocorrelation analysis, the results depict a high clustering within the housing distribution as the value of Moran’s I is 0.993852 which is highly positive and near to 1. Next, the LISA cluster map had successfully identified individual clusters where the housing characteristics and location characteristics in Skudai and Pasir Gudang are similar to its nearby housing units. However, the housing units in Johor Bahru have dissimilar characteristics of housing and locations with its nearest housing units. Based on this analysis, although the housing distribution indicates a clustering pattern overall, the type of clustering is however, locally different. Apart from that, the results also reflects buyer’s preferences of housing choices through locational characteristics whilst suggesting for possibility of conducting distance matrix for further analysis.

Client influence on property valuation has been an emerging theme of behavioural research in the real estate discipline. Studies on valuers’ decision-making behaviour imply that client influence is an important source of judgemental bias. Academic interest in client influence research has evolved from identifying the existence of client pressure to studies that explain the mechanism of client influence. A questionnaire survey was administered to valuers to measure their perception with regard to factors affecting client influence in Malaysia. The effect of client size and size of value adjustment requested by clients on valuation were also tested in a behavioural experiment. The survey revealed that valuers in Malaysia perceived client characteristics and valuer characteristics as some of the most important factors affecting client influence on valuations. It was found that factors such as type of client, size of client, integrity of valuer and experience of valuer could potentially impact on the amount and type of influence exerted on valuations. The results of the logistic regression model indicated that neither the client size nor magnitude of value adjustment requested by client affected the decisions of valuers to alter valuation outcome.