Search results

People are starting to doubt business recovery in office market after global financial crisis in 2008. And most institutional and individual portfolios are undiversified in real estate : incurring enormous transaction costs than other financial products. Especially office market has more risks than other real estate market for high transaction costs and many related industries. Although this stuff can lead to increase the risk of investment in office market, nor are there markets that would allow individuals and institutions to hedge their risks. The establishment of office price index future and option markets is likely to hedge away large risk in office market, and provides lowering transaction costs for trading in real estate. We estimate Office Price Index using S&P/Case-Shiller repeat sale estimator which is proposed by Robert. J. Shiller in 1991. The estimator is a sort of Feasible Generalized Two Stage Least Squares, which compute Value weighted Arithmetic Price Index.

The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, which may be many times self-serving.

By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price.

The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments.

Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.

This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

Acknowledging that housing forms a large part of households’ and country’s long-term wealth, the South African Government has implemented various housing-related policies towards that end. Among these, the government has extended transfer duty exemption to house buyers – both individuals or natural persons and companies or other parties – to enable them buy houses of their choices since January 1950 to date. This paper aims to investigate the relationship between historical transfer duty exemption and housing demand in the City of Johannesburg (CoJ) over a longer period, where a comprehensive data set on house sales and other predictors was available.

This paper uses multi-year data on repeat house sales from 2010 to 2020 and other macro- and socio-economic variables to test the relationship between transfer duty exemption and housing demand in the CoJ, a core part of Gauteng province, South Africa. After cleaning the original data, final analysis was based on 139,121 repeat sales transactions. Data was analyzed in R.

Findings suggest that, when macro-, socio-economic and yearly effects are controlled, transfer duty has a damping effect on housing demand in the CoJ. The results were consistent across all the estimated models. While the motivation behind the implementation of transfer duty exemption in South Africa continues to encourage home ownership, these findings are unexpected because they do not offer support to that policy intention. These unexpected results are partly explained by the prevailing complexities of the housing market and related policies and the progressive tax regime. However, there are welfare effects that all buyers achieve across the housing market ecosystem.

This paper extends work on housing markets research in South Africa through the investigation of mortgage-based housing market in the CoJ that presents one of the densest, developed, bustling and growing housing market in the country. It also presents a fertile ground where all the effects of all the housing policies coalesce – in the statistical sense, one can control the effect of some aspects of housing policies, while appropriately testing the link between a specific policy (in this case, transfer duty exemption) and housing dynamics.

The purpose of this study is to determine whether the fine wine market is efficient between homogeneous lots and heterogeneous lots.

Auction price data for homogeneous (or solid) lots of fine wines was analyzed to create price prediction models. Those models were used to predict the expected auction price for the bottles within heterogeneous lots. Lastly, models were created to explain and predict the differences between expected and realized prices for heterogenous wine lots.

The results show that large inefficiencies exist. The more complex and expensive the heterogeneous lot, the greater the discount relative to what would have been realized if the bottles had been sold individually. This discount can exceed 50% of the expected auction price.

Heterogeneous lots may arise as a practical requirement from the auction house. Restaurant buyers probably have little interest in such lots because of the inclusion of wines the restaurant will be unable to sell. Collectors may be uniquely positioned to benefit from this price discount.

These results are unique in the literature, because the price dynamics of heterogeneous (or mixed) lots of fine wines have not previously been studied.

The Bass model was created to analyse the product life cycle (PLC) in order to help sales and marketing departments in their business decision making. The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

In this research, the authors analysed the 223,577 clients assisted by a nationwide network of car dealerships, who acquired 36,819 vehicles, during a 24-month period. In the analysis, the Bass model was applied to define the PLC phases; and nonlinear regression models were used to carry out the estimations.

The results show that more consistent estimates of the PLC phases are obtained from the clients assisted variable. This work has theoretical and practical implications that can help business management.

The most remarkable thing about this research is that we have shown that the functionality of the clients assisted variable is greater than the sales variable for the Bass model and, therefore, for PLC estimation.

The results of this research are very useful, since they allow marketing decision makers to obtain more consistent estimations of the PLC phases using the Bass model and the clients assisted variable. This is based on the fact that the use of this variable helps to detect if there is any deficiency in the design of the marketing strategy when the client does not make the purchase.

The data on clients assisted are as easily available to companies as sales data. However, the use of this variable improves PLC analysis and this allows an improvement in company forecasting. Thus, making the clients assisted variable a tool to strategically plan investments in innovation and marketing would reduce uncertainty in business management.

The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

This study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.

The authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.

The sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.

Digital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.

Multi-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.

This study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.