REITs are stocks that have underlying real estate assets, priced in the financial market, whose returns behave like direct properties in the long term (Hoesli & Oikarinen, 2012). However, the linkage of returns between listed real estate market, such as REITs, and the stock market varies in different countries. Particularly, Hoesli and Oikarinen (2012) find no tight links of returns in the Australian market. Previous studies mainly focus on the return volatilities in the US markets (Clayton & MacKinnon, 2003; Yang et al., 2012), while it is unclear what the return volatility and spillover effects are between the listed real estate market and the stock market in Australia, and how the pricing factors of REITs reflect the change of such linkages. The key research question is, how does this linkage change in different regimes (market states) of the two markets? By integrating the behavioural finance theory, this research add knowledge to the interpretation of the volatilities of returns transmission between the Australian real estate securities market and the capital market in peaks and Troughs.

This paper aims to improve the explanatory power of the traditional asset pricing model by quantifying and incorporating the human elements, thereby providing another perspective for real estate fund managers to generate appropriate investment strategies. This study is designed to assess the hypothesis that REIT and stock markets share certain periods of crisis regimes and have different stable regimes respectively. To investigate different regimes of REITs and stocks, it is essential to first identify the return sensitivity and volatility of REIT and stock with CAPM (Capital Asset Pricing Model) and ARCH (Autoregressive Conditional Heteroskedasticity) model. Together with the analysis of spillover effects, the regime switch study provides an overall picture of how market state will change given current market condition. The modification to the CAPM model considering the behavioural factors and market anomalies presents a new direction for listed real estate research.

Arbitrage is another key factor in asset pricing, but the real estate market is subject to limits to arbitrage. Fundamental risk, implementation costs, and discrepancies persist in the securities market when arbitrage opportunities seem profitable but the risks arise from illiquidity and volatility pose limits to arbitrage (Shleifer & Vishny, 1997). Globally, REITs have grown at around 17 per cent from 2007 to 2014, primarily driven by equity REITs (McKinsey 2015). Australia has the second-largest REITs market in Asia and is considered a near mature market with a ten-year average yield of 5.2% (Savills 2019).

The effect of proximity to the transit system on property values has become a key issue of discussion on public infrastructure and economic development. This article aims to examine the impact of selected stations on the blue line of Delhi mass rapid transit system (MRTS). The impact was observed on 1256 commercial property sold before and after the commissioning of these stations on the blue line in 2005. Hedonic Price Analysis (HPA) models are used to control other characteristics, such as structural, environmental, locational, neighbourhood and accessibilities in estimating the effects of proximity to metro rail on commercial property values. The method is applied to two time periods from 2000 to 2008 that coincide with the planning and construction (pre-commissioning stage) and operation phase (post- commissioning stage) of the metro rail system. Using sale prices of commercial units near metro rail stations, the study reveals the potential effects on values in the existing commercial property market. The results indicated that the station node shows a negative trend except one during the planning and construction period. The possible reason may be due to traffic, noise, pollution and poor collection of commuters by the existing Delhi bus transportation system. During operation of metro rail system, the results showed that it has produced a significant price premium associated with nearby commercial properties, realised due to improved accessibility, strong collection of users, and a new mass transit system. The coefficients show that the metro rail has induced increase in price premium by 732.7978 ₹to 246.1906₹ (station-wise) in the vicinity and the impact extends to almost 500 meters away from MRTS stations.

The general aim of this research project is to point out a series of essential and relevant elements to have a better understanding on several issues of valuation. His particular purpose is to highlight the content of a significant and actual valuation technique, i.e. Automated Valuation Model, and the possibility of its application in Cluj-Napoca, which is one of the most dynamic cities in Romania, especially when it comes to residential buildings. In our paper we will try to create an algorithm and for that we will use a software program, and in the end, we expect to simplify the valuation process.

Property owners rely on state institutions to enforce claims to rent associated with real property ownership. However, the COVID-19 pandemic is reorganizing how state institutions sort and enforce owner and occupant claims to property. The COVID-19 pandemic has resulted in eviction and credit-reporting restrictions across the United States, which bolster the recognition of occupant claims to property and limit owner claims to rent associated with real property ownership. In this paper, I advocate that a claims-driven approach better reflects how real property ownership relies on state institutions' active enforcement of owners claims to land and improvements. Buiding from a comparison between the global financial crisis, I detail how the COVID-19 pandemic has resulted in state institutions adjusting frameworks and policies which property owners rely on to collect rent -- bolstering state recognition of occupant claims to property. From this, I suggest that literature should more systematically account for how state institutions' frameworks and policies actively shape owners' abilities to extract rent from property, shaping the value of income-producing commercial properties.

Building upon the theory of a classical buyer-seller relation, we analyze empirically the spatial relation between coworking spaces and start-ups in an entrepreneurial ecosystemby addressing product market competition among coworking spaces and start-ups' life cycle stages as drivers of the level of trust among those partners. In our hand-collected sample of coworking spaces in Germany's seven biggest cities, our findings indicate the higher product market competition among coworking spaces in a region is, the more mature start-ups (identified by receiving ex- ternal financing) are likely located in this region. Nascent start-ups are found to be related to product market competition among coworking spaces in a hump-shaped pattern indicating that they are more likely tenants of coworking spaces if market competition of coworking spaces is neither too low nor too high. A European study with WeWork data supports our findings and a difference-in-differences approach mitigates endogeneity concerns.

Housing is a large component of an average household budget and an integral part of Australia’s economy, yet the past 30 years have witnessed a sharp decline in housing affordability with a price-to-income ratio amongst the highest in the world. Faced with an unstable, globalised labour market, decreased governmental support, and wage stagnation arising out of the current financialised neo-liberal environment, households have been reshaped into risk-bearing, self-responsibilised entities, accepting the discourse of commodified housing as their new path to wealth and financial security. The growing house price-wage gap has been managed through the normalisation of debt, facilitated by an evolution of the lending landscape aided by new technologies. The consequently high levels of household indebtedness are recognised as contributing to a plethora of ills, including family and health issues, growing social inequality, and debt exposure risks reminiscent of the Global Financial Crisis. The risk and unsustainability inherent in this situation to both households and the greater economy have been dramatically exposed during the pandemic crisis of 2020, highlighting the importance of maintaining oversight of changes to the lending landscape, including new initiatives which facilitate debt access.

One such initiative is the entry into the housing finance market in September 2017 of digital property media company REA Group, owner of Australian market leader property search platform realestate.com.auMajority owned by media conglomerate News Corporation and partnered with National Australia Bank (NAB), REA offers access through their website to both their own brokerage company and directly to NAB lending, creating Australia’s first end-to-end digital property search and financing journey. Supported and cross-promoted by its powerful media owner, REA plays simultaneous roles as platform designer, content provider, advertising manager, purveyor of active financial ‘product’, and recipient of advertising, product and data revenue streams.

In analyzing the housing market, capturing the temporal change of the market structure is of importance. Shimizu et al. (2010) [1] showed that the hedonic price index works well to capture it. Pioneering work on the hedonic price index was done by Court (1939) [2], and Griliches (1961) [3] popularized it. In these works, time dummy variables play an important role to estimate the temporal change. To estimate the change of the time dummy coefficients, Shimizu et al. (2010) [4] proposed an overlapping-period hedonic model (OPHM), whose method smooths the coefficients with a rolling window, similar to the moving average method in time series analysis. However, this method is not available to forecast the dynamic change of prices and cannot consider cyclic systems, such as seasonal effects, to smooth coefficients. To overcome these limitations, modelling dynamic changes of coefficients is one of the possible methods.

Additionally, the works above mainly focused on the temporal the change of coefficient of the constant term. However, because other coefficients may also change temporally, we consider the change in all coefficients. The method was applied to a housing market by Guirguis et al.(2005) [5], but the authors did not focus on hedonic analysis. Therefore, in this study, we compare dynamic modelling with other hedonic methods to examine its features.

A hedonic linear regression model using time-varying coefficients is described as follows

y^ᶯt = β ^{T x,nt + E n,t}

We denote the price of the nth housing unit at time t as yn,t, and its housing attributes as xn,t. The hedonic coefficient at time t are described by Bt and change dynamically. En,t is independently and identically distributed Gaussian random noise.

Time dummy type models do not allow for the temporal change in coefficients except for the constant term. However, the model above allow all coefficients to change temporally. Therefore this is the extended version of time dummy models.

There are some possible methods to estimate Bt. The naïve method is to estimate Eq. (1) using the date for each time t. This method is called the “Separate Hedonic Model” in this paper. A similar method was applied in

Building energy consumption has become an important issue in China. As major energy consumers, commercial buildings have become the target for energy efficiency retrofit. Energy Performance Contract (EPC) is a new mechanism in China which offers a series of retrofit strategies and financial investments by energy service companies (ESCOs). This mechanism, however, has not been widely used by the current building energy efficiency market although the Chinese government has published a compulsory policy and completed a second EPC demonstration program. In order to establish the reasons why the market has not reacted expectedly, this research aims to examine the nature and characteristic of the EPC market, challenges/barriers, and the development of an incentive mechanism to encourage the use of EPC for the commercial building sector in China. The commercial building sector is selected as the subject of the research as the potential for energy savings and property right structure.

The research is led by three main research questions: 1) What is the current state of the EPC market in the Chinese building sector.2) What are the factors limiting the uptake of the EPC of CBEER in China.3) How can the Transaction Cost Theory be adapted to incentivise the uptake of EPC in such an imperfect market. The objectivist epistemology and positivist theoretical perspective are chosen as philosophical assumptions. Quantitative paradigm is chosen as the research approach considering this research is going to test the imperfect market theorem and the deductive research inquiry process. Multiple research methods including qualitative and quantitative practices are applied to the collection of data.Firstly, the research starts with reviewing literature associated with the EPC market to develop an analytical framework for understanding the current situation and limitations of the EPC market in commercial buildings. Secondly, the research involves a questionnaire survey and interviews to test the theory of imperfect market. An online questionnaire survey is conducted to identify challenges/barriers and semi-structured interviews are used to examine reasons contributing to challenges/barriers. Thirdly, the transaction cost theory is used to develop a theoretical framework as a mechanism for incentivising the EPC market in China. Case studies selected from successful pilot EPC projects in China are developed to examine the robustness of the theoretical framework.

The quantitative analyses were stemming from ANOVA test and factor analysis. The ANOVA test showed there’s a significant difference towards the effects and difficulties of using EPC between the building owners and ESCOs. Furthermore, the findings from factor analysis identified nine factors impacting the uptake of EPC: unclear EPC costs and benefits, the complicity of commercial building energy efficiency retrofits, incomplete EPC contract, uncertain energy savings, high criteria for ESCOs, policies and regulations, financial support, service quality of ESCOs, and the number of ESCOs.The interviews analysed through thematic analysis showed the reasons contributing to these factors. The incomplete information is the major trigger of the market failure of EPC which will also lead to market power. Meanwhile, the externalities of building energy consumption couldn’t be internalised only based on a market mechanism (like EPC) and need more government intervention to deal with. Subsidies,taxes, and regulations are designed to decrease the transaction costs caused by the incomplete information and externalities to play the role of the EPC market. 

The outcome of this research looks to formalise theories of the characteristics of the imperfect EPC market and adapts transaction cost theory as an incentive policy-design tool providing fairness between stakeholders. It provides policymakers with a framework for generating market incentives in the EPC market and offers information to EPC stakeholders on the current market situation and potentially make EPC more popular for building energy efficiency in China.

Inability to pay energy bills, limiting consumption to the detriment of health, and spending a high proportion of income on energy is known as energy poverty (EP). Low income, older households are among the most vulnerable, as they often live in old poorly designed homes and lack the capital and the agency to upgrade them. Adding to that, most of Australia’s existing housing stock is old and was constructed prior to the introduction of the first national housing minimum energy efficiency requirements. This study extends the knowledge about EP in Australia with a mixed-methods approach in two phases: in-depth interviews with Age pensioners experiencing EP in Sydney, to explore how EP affects – and is affected by – their capabilities; and a quantitative analysis of secondary data (the Australian Housing Conditions Dataset) to investigate the extent of EP among Age pensioners and their housing circumstances.

The recent outbreak of the COVID-19 pandemic has caused unprecedented public health and economic uncertainty creating a substantial spike in fear and negative sentiment. In this paper we attempt to capture this COVID-19-induced aggregate negative sentiment and present a first perspective on the relationship between sentiment and REIT returns during these turbulent times.

Most of the sentiment measures used in real estate literature are survey-based and are administered either monthly or quarterly. The uncertainty induced by COVID-19 has been changing so rapidly within a short span of time that requires capturing changes in sentiment daily. We therefore believe an aggregate sentiment measure computed from daily Google search frequency provides a more accurate and dynamic measure. We construct two measures of COVID-19 related sentiment. Our methodology in constructing such an index is built on the FEARS index constructed by Da, Engelberg and Gao (2015), who use their index to measure its impact on stock market returns, market volatility, and mutual fund flows. 

The first measure captures the health-related fear and panic caused by COVID-19 and we call this measure, Health Fear Index. The second one is a measure of aggregate sentiment related to fear and uncertainty due to the concerns arising from the state of the economy and we term this measure, Economic Fear Index. We then analyze their relationship with REIT returns. This is one of the first few studies examining the impact of sentiment induced by COVID-19 on REIT returns. 

We find that REITs react inversely to changes in both the Health and Economic Fear Indexes with the Health index has a greater impact indicating that increases in fear related to health has greater negative impact on REIT returns than the fear caused by economic downturn. We find that one standard deviation increase in Economic Fear Index leads to 16 percentage point decrease in average daily return of REITs while increase in one standard deviation in Heath Fear Index results in average daily return to fall by 20.68 percentage points. Both these coefficients are significant at the 1% level. These results are consistent across different REIT sectors.

We also examine the varying impact of these two fear indexes in different REIT sector indexes and we find that all the REIT sectors are responsive to changes in both the Health and Economic Fear Indexes with the Health index having a greater impact as seen in individual REITs. We also find that lodging and healthcare REITs are the most responsive to COVID-19 related fear while infrastructure and timber REITs are the least responsive. We find that one standard deviation increase in Health Fear Index leads to a reduction of 62.18 percentage points in average return for lodging REITs. The corresponding reduction in return of Health care REITs is 60.93 percentage points. The decrease in return of infrastructure REITs and timber REITs for one standard deviation increase in Health Fear index is 39.92 percentage points and 25.23 percentage points, respectively.

Aim of the study: The specific aim of this study is to investigate the relationship between activities taken by office workers (during work hours and daily travels to/from work) within green urban environment and related sustainable workplace engagement level. Therefore, the proposed investigation may have some relevant implications for the real estate and planning sector in terms of physical high quality business infrastructure development, as well as wider contribution to cities’ andbusiness productivity growth. The general research aim is to validate if sustainable workplace engagement associated with green urban environment may have a real value for business enterprise growth in the context of green economy paradigm.

Scope of the study: The South East England represents one of the most entrepreneurial andfinancially developed region in the United Kingdom, significantly contributing to country’s Gross Domestic Product (GDP). It is a home for many global firms’ headquarters and attracts a lot ofemployees from the whole world. However, an agglomeration of businesses and human population poses threats for both human health and environmental quality. Hence, the high quality workplace environments have been specifically chosen due to provision of green urban environments for their employees. The study will be focused on three case studies based in Business Parks in Reading (Berkshire), namely: Green Park Business Park, Winnersh Triangle and Arlington Business Park. The proposed investigation will look at the scale of Business Park itself (actively used by employees during work hours) and wider city/regional impact (related to daily work travels). Although the scope of research will be based on the chosen case studies, in fact will be extended to wider city/regional context. The proposed scope of the study will help to position the physical locations of Business Parks within larger infrastructural investments, which may have a critical value for workplace engagement and green growth in general. Giving the fact that human health and environmental quality are interlinked and strongly interrelated issues, the broader perspective may be useful to fully assess how business operates within this landscape and how this impacts its economic performance. In general, the study will look at the possibility of merging entrepreneurial and green aspirations of cities in the context of global economy growth.

The dividend pricing hypothesis, as applied to housing, argues that the total return to housing is properly measured as the sum of its rental and appreciation returns. This paper discusses specific properties of the relation between the two components of the total return using the estimates of these rates across US MASAs. Im-Pesaran-Shin panel unit root tests and augmented Dickey-Fuller regressions demonstrate that differences in asset appreciation returns are systematically associated with location and there is persistence in returns. These returns suggested that the difference in appreciation rates are predictable so that differences in appreciation rates have a component that is anticipated. The innovation in this paper is that shocks to total housing return are constructed and related to household consumption. Empirical tests following the Case-Quigley-Shiller framework examine the housing wealth effect on consumption, using total versus appreciation returns. The principal finding is that the effect of a housing return shock on consumption is much larger when it is measured as an innovation in the total return to housing, implying that the CAP rate component of return is important. The effect of a housing return shock on consumption is also larger than that of the stock market wealth shock.

We examine whether systematic tail risk premium exists in the cross-section of real estate investment trusts. Using US equity REITs data from 1993 to 2018, we obtain the systematic tail risk, which is “industrial tail exposure risk (ITER)”, based on extreme value theory. We find that REITs in higher ITER decile outperform REITs in lowest ITER decile by 10.34% per annum. The impact of ITER remains significant after controlling for well-known firm and return factors. The positive return premium is not explained by traditional systematic tail risk based on the left-tail of the market return. Thus, our results suggest that REITs investors are averse to crash events, primarily associated with multiple industries rather than the aggregate market alone.

The typical SEC's reaction to a crisis is strengthening disclosure requirements mandating firms to inform investors about their assessment of future contingencies. This should enable investors to monitor risks a firm is facing. However lengthy and complex disclosures -- mostly for dozens or hundreds of firms in an investor's portfolio -- can hardly be processed by a human. Additionally, it is unclear if investors follow regulatory requirements or disclosures are merely boilerplates giving the investor a limited view. It would be informative for investors to see the firm's risk assessment regarding these kinds of risks. To cope with the flood of information, we propose using an unsupervised machine learning algorithm to identify and quantify the risk factor topics discussed in the SEC's 10-K filing. We apply this algorithm (Structural Topic Model, STM) to the Item 1A and Item 7A of the US REITs' 10-K's filings between 2005 and 2019. Our results suggest, that STM is advantageous over the traditional method since it finds clearer and consequently more meaningful risk factor topics beyond the investment foci of REITs. Furthermore, we investigate whether and how the identified topics affect the risk perception of investors after the filing date. We find all three kinds of topics: uninformative topics with no impact (null argument), increasing risk perception topics (divergence argument), and decreasing risk perception topics (convergence argument) -- the majority. Overall, our results suggest that REIT managers use risk disclosures to reveal previously unknown information that has not yet been incorporated into market prices in the short run; but they diminish in the long run.

This paper investigates asset allocation to stocks, real estate, bonds and treasury bills under in the presence of regime switching. The joint distribution of asset returns is captured by a four-regime framework of bull, high volatile, slow growth and crash states in a four-variable model and eight-variables framework. Investors tend to allocate significant proportion of wealth to equity REITs regardless of the initial states, especially in the long term. Moreover, when accounting for horizon of investment in different regimes, investors allocate more to the risky assets than risk-free asset. Next I will study whether horizontal effects can be captured after adding Markov-switching framework to the model, proving the predictability power of switching regimes.

Today the greatest technological development in action, common to all sectors, is digitalization. Within the real estate sector, which has always been one of the least innovative sectors,digitalization is bringing radical changes both in terms of production and services. Among the innovations underway, there is the implementation of Automated Valuation Models (AVM) through self-learning techniques. Their use can potentially transform the current state of assessment services.

Traditionally, Automated Valuation Models use econometric models. They are based on hedonic price theory, which assigns a marginal price to each characteristic of the asset or its context. Machine learning models, on the contrary, learn directly from the data and take the form that best suits them.

Many operators in the sector, while admitting that they do not know enough about the phenomenon, are sceptical about the use of AVM. The issue is still the domain of scientific research or of few players. There is a lack of knowledge sharing with the whole appraisal sector. The objective of this research is to provide assessors with some empirical evidence that can help them to gain confidence in these models and discover their potential.

Purpose: The study examined the volatility of daily market price of listed property stocks on Johannesburg Stock Exchange (JSE) for 10year period (2008-2017). The primary aim of the study is to investigate the volatility pattern of the daily market price; in an attempt to document and model the nature of volatility characterised with the daily price of the listed property stock market for informed investment decision making.

Design/Methodology/Approach- The study used daily price from January 2, 2008 to December 29, 2017 of twelve (12) quoted property companies out of the twenty seven (27) listed on Johannesburg Stock Exchange (SA REIT Association, 2020). The property stocks were selected based on the quoted property companies that have sufficient published data on daily price for the period under review. The data were obtained from the JSE published statistical bulletin. The study computed the average daily price of the selected (12) property stocks and was used as proxy for daily market price for property stock market in the analysis. The study deployed mean, standard deviation, maximum and minimum analytical tools for descriptive statistics, Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt- Shin (KPSS); Jarque-Bera, Breusch-Godfrey LM and Heteroskedasticity tests for unit root, normal distribution, autocorrelation and ARCH effect tests respectively. The diversification benefits and modelling of SA-REIT market price volatility were analysed using correlation matrix and generalised autoregressive conditional heteroskedasticity (GARCH 1, 1)

Finding: The daily price of the selected property stocks were stationary at 1st difference I(1) as indicated by results of ADF and KPSS unit root tests (p<.05); the p-value for LM autocorrelation and heteroskedasticity tests were significant (p<.05) indicating that the series is characterised with autocorrelation and ARCH effect; and exhibited non- normal distribution pattern as reported by Jarque-Bera statistics (p<.05). The correlation matrix showed strong diversification benefits between EQUP and INTU (-0.809) property stocks. Analysis of residual estimate of the series documents the evidence of volatility characterised with prolong high and low clustering pattern. The GARCH model reported that, the previous days information of both the daily market price (ARCH term) and the volatility (GARCH term) have positive and significant (p<.05) effect on current day's market price volatility in the property stock market. The result of the model implies that investment in property stock market is strongly driven by positive news on daily price than negative shock; meaning that South Afric ponse to good news on daily market price than bad news when thinking of investing in listed property company shares on Johannesburg Stock Exchange.

Practical implications: The study documents and models the statistical significant influence of conditional variance (volatility) of daily price of South Africa property stock market.

Originality/Value: The study added to the exiting body of knowledge by documenting the volatility pattern and model structure of SA-property stock markets for informed investment decision making.

We use an asset pricing model based on net value to study the asset-specific risk factors of private commercial properties across local markets. To accomplish this, we extend the approach by Korteweg and Nagel (2016) into a pricing setting where the objective is to minimize the pricing errors by a thorough examination of factors. Location, despite being an important consideration both in theory and in practice, remains to be developed into well-defined risk factors. In particular, the risk premium of location risk awaits further investigation. We first investigate an all-encompassing location factor constructed from locational value estimates using local polynomial regressions. Then we attempt to further disentangle between macro-and micro- location risk factors. Macro-location risk represents regional macroeconomic conditions, while our micro-location factor reflects accessibility and amenities offered by a specific location. We also test our location factors against stock market risk factors.

This paper uses a difference-in-differences (DID) approach to identify the effect of proximity to COVID-19 cases on the returns of real estate firms. We use a novel micro level dataset which combines extensive data on the geographic footprint of COVID-19 patients, i.e. the locations they have resided in or visited, and the location of property holdings of real estate firms in Hong Kong. We find significantly negative effects of proximity to COVID-19 cases on stock returns. Having a property within 2 miles from a COVID-19 case results in a 0.02% lower return one day after the case disclosure. This effect is stronger for properties located closer and is weaker if the property is a residential building.

This paper is making empirical investigation of transmission of volatility shocks across the space and time, by using spatio-temporal form of GARCH (Generalized autoregressive conditional heteroscedastic) processes. Use of spatial-temporal weights allow both, past errors and past conditional variances in neighboring locations, to have differential impact on each current estimated variance at different transaction location. Hence, this spatio-temporal setting allow one to model both, the “shock” and “memory” of the conditional volatility. Estimated parameters using municipal housing transactions from Jonkoping, Sweden from 2015 through 2019 shows positive memory effect, along with positive shock and confirm the assumption of spatio-temporal persistence in the variance. Omitting the spatial GARCH element brings to the underestimation of the conditional variance. This finding favor the use of transactional spatial model vs aggregated model. Market for the houses has different spatio-temoral dynamics compared with the market for the apartments, showing stronger immediate spatial response to the shocks in the near neighborhood, but lower spatial memory (persistence) of the past close-by shocks.

1. Introduction: This is an empirical, quantitative UK study using Rightmove’s online housing search data. Rightmove.co.uk is the UK’s largest online real estate portal and property website, and particularly in England and Wales, holds more than 90% of properties that enter the market. The data analysed is generated by the users of the website who can search for housing through the Draw-A-Search, resulting in over 4.5million visits per day. Users are able to change attributes such as house price range, number of bedrooms, or property type, as well as applying filters such as the property requiring a garden, garage or being a new build property, for example. The drawn searches require the users to match their mental maps with respect to Google map data in the Rightmove website. The aim of the project is to assess the extent to which online housing search data can help us identify, spatially, housing market and sub-market areas through the use of user-generated search data. Utilising Rightmove’s search data will result in an analysis on the gap in knowledge of the geography of housing search in relation to the geography of choice and demand. This paper focuses on Greater London in order to increase understanding of the user-drawn housing market in the UK.

2. Conceptual Framework of the Study: There is a gap in research in how people search for housing online, and the supply and demand statistics related to this search. It assumes that those people who are actively searching for a house all want the same attributes of buying and selling houses (number of bedrooms, price, moving in/out date) as everyone else in society which creates a market equilibrium- but this is not the case. There is already substantial previous research on the housing market, yet the majority of the research is prior to 1990. This is consequently outdated as since then the prominence of the online housing market has emerged. Publicly available Google trends data and online search data have thus advanced this method to that of prediction locations (Beracha and Wintoki, 2013; Choi and Varian, 2012; Wu and Brynjolfsson, 2009). This has proven to be of little avail as the majority of research draws to nowcasting instead of predicting the future, which is where the gap in literature and methods lies. Research has tended to focus on where people have moved to, as opposed to where a searcher might want to move to (Rae, 2015) so this project will delve deeper into this latent versus revealed demand argument.

3. Study Area and Data: The project focuses on Greater London in the UK. Greater London, for this study, will be treated as a separate case, given its uniqueness within the English housing market and its significantly different characteristics with respect to price and geography, which Gray (2017) also defended. The aim of the project is to assess the extent to which online housing search data can help us identify, spatially, housing market and sub-market areas through the use of user-generated search data. The project takes a quantitative approach, using deductive reasoning, empirical evidence and hypothesis testing. The project primarily uses secondary data provided by Rightmove plc. The data is generated by users of the website searching for properties. The data analysed focuses on the Draw-A-Search Tool. The data used is from the month of March from 2012-15 for the drawn searches as March is when people typically choose to buy a house so there are more data to analyse. The formula used for the analysis () is called the Polsby-Popper Ratio which gives a result between [0,1]. A score of 1 means that it is a complete circle and has maximal compactness, whereas a score of 0 means that there is a complete lack of compactness. This analysis conducts how irregular a shape is drawn within the Rightmove Draw-A-Search Tool. Some examples are shown in Figure 1, where it can be seen that from left to right the last four shapes diverge from a perfect circle. It has been assumed that the lower the score, the more knowledge a user has about a location. This will be explored in the analysis.

4. Results: Figure 2 shows that the majority of drawn searches within the Greater London boundary from March 2015 fall within the 0.7-0.8 ratio, meaning that most users are drawing regular shapes such as rectangles, than opposed to irregular shapes that look like paint splatters. Figure 3 shows a violin plot for 2012-2015 compactness scores in London, and it shows that there is little change in the way users are drawing shapes. It can then be assumed that the majority of users are first time buyers within London, as opposed to moving within London. A very small amount of users (~1,000 polygons) have a ratio of <0.1 which means they have based their search around the London underground stations, green parks or specific streets. This can conclude that these users have good prior knowledge of the desired property location. The data also reveals where the polygons overlap, as shown in Figure 5, and this shows where the most drawn demand is and this has been split into detached, semi-detached and flat property types. It shows that within London, the most drawn property type is a flat and this covers almost all of the Greater London boundary. A smaller scale can also be applied, as shown in Figure 4. This shows a user drawn shape along the River Thames so they wish to live in a property with a river view. Similar results have been found along bus/tram/underground/train routes, and also ommiting specfic areas such as whole housing estates.

5. Conclusion: This new source of data therefore also allows us to explore from a technical point of view how the technology of housing market search might be helping shape or influencing peoples search behaviour. One of the ways that this can be achieved is by looking at the way in which people draw shapes, and how intricate they are compared to how big they are. To interpret the intricacy of the shapes drawn as a measure of how engaged or specific people are in their search some geometry is needed. The Polsby-Popper score gives some indication of the intricateness of shapes which is explained in the results. It it is also interesting to consider whether technology is hindering or helping the online housing search, and whether their knowledge of mental maps is suitable to finding their dream property. The majority of people are searching in broad, larger areas, as opposed to smaller intricate drawn shapes that limit the number of properties returned. It is then estimated that most people are moving to new locations that they are not familiar with, as opposed to staying in the same location. The search versus actual sales has been correlated on a six-month lag and has a strong positive correlation, so it can be concluded that the drawn search demand can accurately predict the future sold property outcomes.

Homeowners’ property tax payments are commonly derived as a fraction of their homes’ estimated market values (EMVs). In theory, these EMVs should impact trading prices through two counteracting channels. First, an increase in EMV implies increased tax payments, which should negatively affect a home’s trading price (tax channel). Second, EMVs are a potential reference price, which should lead to a positive effect (anchoring channel). In a quasi-experimental setting that exploits geographic variation in timing of EMV publications and revaluation frequencies, I show that a.c.p. higher EMV leads to a lower trading price, i.e., that the tax channel dominates.

This study investigates the impact of the Lagos Metropolitan Development and Governance Project (LMDGP) on residential property rental values in Lagos State, Nigeria. The study adopts a survey approach to determine the effect of the LMDGP on blocks of flats using the hedonic analysis. A total of 385 blocks of flats were selected for both pre and post regeneration project. Results show that the property rental value was significantly higher post regeneration compared to pre urban regeneration period (t=28.252,p < 0.001). The hedonic model (Ordinary Least Squares) accounted for higher variations in the rental value in the post urban regeneration period (R²  = 74.7%) than in pre urban regeneration period (R²  = 68.7%). Further findings show that the residential housing conditions most significantly impacted by the urban regeneration projects are; decline in traffic congestion, easy access, and adequate street lighting. Stakeholders perception on urban regeneration projects that impacted significantly on rental values are; construction of new school classroom blocks, drainage channels, and roads. Economic and political factors amongst others influence the implementation of urban regeneration projects.

This study examines the determinants of herding in the U.S. housing market with the focus on the effect of psychological biases on herding. People are motivated to copy others due to a psychological need for social behaviour. If homebuyers see large local inequalities in homeownership, they will have a stronger desire to overconsume relative to areas with more equal housing distribution. This mimetic behaviour will therefore lead to higher levels of herding, with resultant poor economic outcomes. Therefore, we test if areas with more extreme distributions of housing, income and consumption (i.e. areas where people have greater degree of positional concern) will exhibit more extreme herding, while controlling for fundamental economic and housing market variables.

The development of behavioural finance in the 1970s (Kahneman and Tversky 1979) and then its crossover into asset markets shortly after (Shiller 1982) has allowed behavioural factors in real estate to enjoy a growing research interest. It has been shown that an appreciation of sociological and psychological factors provides an understanding of market dynamics beyond the conventional efficient markets approach (West 1988, Lux 1995).

Herding is defined when behaviour is correlated across individuals (Devenow and Welch 1996), especially where it leads to sub-optimal investment decisions and bubble formation. Herding can be rational if informational inefficiency and agency issues lead investors to copy others (Bikhchandani, Hirshleifer et al. 1998), or irrational when they are susceptible to psychological biases (Devenow and Welch 1996).

One motivator for the presence of these psychological biases is the established economic presence of positional goods, whose value is derived from their social status and where the value is based on relative, rather than absolute, distribution. The overconsumption of positional goods creates negative externalities which have wider economic repercussions (Frank 2005). It has been shown that excessive social positional concerns lead to poor outcomes in physical and mental health (Frank 2008), and also sub-optimal investment decisions, such that people over-consume and over-leverage.

Evidence for the presence of herding in institutional investors is mixed (Barber, Odean et al. 2009), and the research in direct residential markets is limited, however the latter has found some significant results (Hott 2012, Ngene, Sohn et al. 2017). The existing evidence suggests that higher levels of herding can lead to bubble formation, and the resulting welfare loss can be extensive (Deng, Hung et al. 2018), as most clearly seen in the fallout from the Global Financial Crisis.Considering the scale of residential property as an asset class (over $33 trillion in the USA alone), and the dual nature of housing as an investment asset and consumption good, then there is an obvious requirement for further research.

This paper investigates the relationship between strategic default in the US residential mortgage market and household portfolio composition after the Great Recession in 2007. Following the definition of strategic default proposed by Gerardi et al. (2018), we find that in addition to the well-known ‘Double Triggers’ of negative equity and payment ability, households’ portfolio composition can also affect their strategic default decision. Holding a larger amount of non housing durable assets increases the probability of strategic default, while owning more liquid assets can reduce the probability of default through two potential channels: portfolio rebalancing and relative cost of default.

The research problem addressed in the paper is the occurrence of undeveloped land on Silesian Metropolitan Area (MA Silesia), what is regarded as high importance for its implication on cities’ spatial order and real estate market. The purpose of the article is to investigate the nature of withholding the land from market and to provide the recommendations to municipalties’ authorities, likewise to contribute to the literature of time of land development. We conduct the analyse of elapse of time between acquisition of plots of land and application for the building permits among different market participants. We use the quantitative approach to research, particularly the survival analysis originated from statistical methods. Investigated data bases on merged two dataset: sales of landed properties and building permit decisions in MA Silesia, in year 2014-2019.  The most important findings are that the hazard of applying for building permits (proxy of developing the and) is higher when the market participant. Investors apply for building permits faster with plots of land for which Spatial Development Plan is approved than with those without one. One unit increase in price of land is associated with a 47% increase in hazard rate of land development. As our study shows that withholding the land occurs on MA Silesia, our recommendations for municipalities are the following: to use perpetual usufruct institution more efficiently, elaborate spatial development plans for whole metropolitan area, re-acquire land within 2-5 years if it is not developed, implement charges for not developing land in specific time, require of bank guarantee for finance construction before land allocation, and in general to run spatial policy in more sustainable direction, not focusing just to attract investors.

This paper assesses whether changes in the level and volatility of short- and long-term interest rates divergently affect excess returns of sector-specific REITs in the Pacific Rim region between July 2006 and December 2018. This is due to that different property sectors have distinct risk-return characterises. The generalised autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology was employed to examine the linkage between interest rates and daily excess returns of sector-specific REITs. The empirical results show that sector- specific REITs were less sensitive to short- and long-term interest rate changes between July 2006 and December 2018, compared with diversified REITs in the US, Japan, Australia and Singapore. This may be attributed with a diversified REIT portfolio comprising multiple property sectors. Within sector-specific REITs, retail and residential REITs were susceptible to interest rate movements over the full study period. On the other hand, office and specialty REITs were generally less sensitive to changes in the level and volatility of short- and long- term interest rate series across all markets in the Pacific Rim region. However, the interest rate sensitivity of industrial REITs was somewhat mixed. This sector was sensitive to interest rate movements, but no comparable evidence was found since the onset of GFC. This may be attributed with its changing portfolio structures, due to recent e-commerce trends. These findings are expected to enhance property investors' understanding of the interest rate risk management for different property types of REITs in the US, Japan, Australia and Singapore.