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By 2009 Netflix had all but trounced its traditional bricks-and-mortar competitors in the video rental industry. Since its founding in the late 1990s, the company had changed the face of the industry and threatened the existence of such entrenched giants as Blockbuster, in large part because of its easy-to-understand subscription model, policy of no late fees, and use of analytics to leverage customer data to provide a superior customer experience and grow its e-commerce media platform. Netflix's investment in data collection, IT systems, and advanced analytics such as proprietary data mining techniques and algorithms for customer and product matching played a crucial role in both its strategy and success. However, the explosive growth of the digital media market presents a serious challenge for Netflix's business going forward. How will its analytics, customer data, and customer interaction models play a role in the future of the digital media space? Will it be able to stand up to competition from more seasoned players in the digital market, such as Amazon and Apple? What position must Netflix take in order to successfully compete in this digital arena?

To examine the benefits and risks of investment in analytical technology as a means for mining customer data for business insights. Students will develop a strategy position for Netflix's investment in technology and its digital media business. Students must also consider how new corporate partnerships and changes to the customer channel model will allow the company to prosper in the highly competitive digital space.

The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market.

The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research.

The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models.

The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures.

The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

This paper examines rent determinants and their relationship with commercial office property rents.

The method adopted in this study differs from that of previous studies on this topic. Firstly, based on the survey of the viewpoints of experts, Relative Importance Index (RII) analysis was used to identify rent determinants and to rank and ensure their relevance and validity in the Sri Lankan context. Secondly, sampling of data related to 115 office properties collected from property tenants and landlords located within the central built-up area of Colombo City was conducted using a multi-methods approach to carry out an objective hedonic analysis of office rents.

This research utilizes RII and hedonic models to provide insights into determinants and relationships. Both analyses confirm that the three top drivers of commercial office rent are distance from the major town center, availability of parking space and the condition of the property. In addition to these three factors, hedonic models reveal that the age of the property and the availability of a conference hall also play a relevant role in explaining office rents. Given the disparities in the findings of the two methods, further examination was able to confirm that factors such as distance from the major town center, parking availability, age of the property, presence of a conference hall, building condition, floor size, business type and type of building are likely to influence commercial office rent. These findings reflect elements such as the quality, newness and better facilities of different office properties.

This systematic study and analysis of office rent for the guidance of real estate investors can support sound investment decisions, potentially leading to more financially sound property development, reduced public debt levels and improved public-private financing. Further, the research findings offer valuable insights to real estate investors, developers and planners regarding location decisions for office development quality enhancements in future office developments.

This research provides fresh insights into the local scale office market, an area where limited evidence currently exists. Further, the methodology adopted provides evidence that hedonic analysis, supported by a multi-method approach, can mitigate the subjective judgments made by professionals.

The purpose of this paper is to examine the nature of Singapore's private housing market with respect to its price movement using time series models.

This paper analyses the price dynamics in the Singapore private housing market using the integrated autoregressive‐moving average modeling coupled with outlier detection and autoregressive conditional heteroskedasticity modeling techniques.

The paper finds that private house prices are better modeled as an ARIMA (1, 1, 0) model with corresponding dummy variables. This suggests that housing prices may be characterized as the combination of a stationary cyclical component and a non‐stationary stochastic growth component over the past almost three decades. This affirms that the Singapore's private housing market is characterised by the weak‐form inefficiency.

The results show that even though ARIMA with dummy variables performs better to ARIMA with ARCH in dynamic performance, there is only marginal improvement on the original model. This suggests that the method for selecting intervention variables in the ARIMA modeling is worth further research with the aim of improving its predictive ability.

This paper incorporates the detection of outliers and intervention procedure in the modeling in order to analyse the impacts of extraordinary events such the recent Asian financial crisis and excessive market speculation on property prices and take them into consideration in forecasting price changes.

There are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables.

Using the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418).

The study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas.

The results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity.

The “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling.

In India – the largest democracy and second most populated country globally – the housing research domain is relatively under-researched and under-theorized. To support and advance research in this domain, this study aims to form and organize the repository of extant academic knowledge in the subject matter of housing research in India.

This study uses a scoping review methodology and a thematic analysis method. All the articles analyzed in this study were systematically searched by following the scoping review approach proposed by Arksey and O’Malley (2005). An initial search found 365 articles and finally, 108 articles that met the inclusion criteria were analyzed using the thematic analysis method.

The data extracted from these 108 articles were analyzed using thematic analysis to arrive at four thematic areas, namely, housing policy, slum housing, housing finance and affordable housing. These thematic areas and 11 sub-themes present under them were used to present a thematic map of housing policy research in India.

This paper contributes to presenting an up-to-date literature review of the housing policy research in India.

To the best of our knowledge, this scoping review focused on housing research in India is the first of its kind. We hope that this study provides a repository of extant research on housing research in India to help current and future researchers.

The purpose is to increase knowledge of rent negotiations by investigating differences in beliefs held by property landlords and retailers on factors that they deem important in rent negotiation.

This study investigates differences in subjective beliefs held by landlords and retail trade tenants on factors that affect rent levels during the rent negotiation process using a factor analysis approach. Semi-structured interviews were made with seven large real estate owners/landlords and retailers and eight experts in negotiating retail rent to elicit variables that have an impact on retail rent. Thereafter, a web-based survey was sent to 421 respondents who had experience in rent negotiation. Several factors were extracted using factor analysis. The data collection was made in Sweden during the coronavirus disease 2019 (COVID-19) pandemic in late spring 2021

Significant differences are found in beliefs held by landlords and retail trade tenants in four out of seven-factor: regional growth, e-commerce, customer focus and trust. Landlords rate these factors higher than retailers do. There are also systematic differences between landlords and retailers depending on their education levels on the following factors: rent and vacancies, e-commerce and customer focus. The number of years of experience did not prove to be significant instead differences are found to exist in factors

Not only do traditional factors of importance, such as lease structure, the effect of location, size and anchor or non-anchor tenants, have an effect on negotiated rent levels. Differences in other factors also exist, such as regional growth, e-commerce, customer focus and trust factors that may play an important role in the negation of retail rent.

The findings provide new insights into the different views on factors that affect rent negotiations between landlords and retail tenants. Knowledge of such differences may increase the overall transparency in the negotiation process. Transparency may be increased by putting forward information on these factors before a negotiation takes place, in order to smooth differences in their beliefs.

If transparency in the negotiation process of retail rent increases, time to reach an agreement, stress and anxiety can be reduced by putting forward information on factors where differences exist between landlords and retailers

New insights on retail rent negotiation have been put forward in this research paper. Not only do traditional factors such as lease structure matters, but subjective beliefs on factors such as regional growth and the level of education are also important, as this study has shown using a factors analysis approach.

Downward movements in house prices can exacerbate bank crises if mark‐to‐market methods of asset valuation are used by lenders to assess their current balance sheet exposure. There is an imperative to find methods of house price index calculation that reflect equilibrium prices rather than temporary undershoots. The purpose of this paper is to propose a new methodology in order to evaluate whether market house prices are different from their fundamental asset prices.

This paper proposes a method for house asset valuation that incorporates expected house price appreciation as an endogenous variable. This avoids the necessity to make conjectures about expected future house price appreciation when applying Poterba's user‐cost method of house asset valuation. The methodological extension to Poterba's user‐cost method of house asset valuation endogenises expected house price appreciation as the no‐arbitrage expected price appreciation consistent with the term structure of real interest rates. A benchmark equilibrium house valuation can be calculated because the term structure of real forward interest rates is observable in financial markets. This enables market house prices to be compared with the benchmark equilibrium valuation in order to determine if house prices are overvalued or undervalued.

The paper presents the results of a worked example to illustrate how this approach could be applied in practice.

There are a number of issues associated with the measurement of user cost which we do not address here and which the authors hope will provide fruitful avenues for future research. There are also issues regarding the impact of tax frameworks on the returns to housing, particularly the taxation of mortgage interest and imputed income. More work also needs to be done in comparing the performance of the extended Poterba model against alternative approaches, such as those that use expected inflation and/or long‐run average house price appreciation, or the real interest rate spread to proxy for expected capital appreciation, and how these different approaches compare in different institutional and socio‐economic contexts.

The authors' results underscore the rationale for mortgage banks to use marking to model instead of marking to market, and this in turn should reduce unnecessary macroeconomic instability when the market prices of houses undershoot fundamental value.

The paper shows how the term structure of real forward interest rates, observable in financial markets, can be used to extend the Poterba model.

Many corporates in India are constantly adapting real estate benchmarks to reduce the workspace maintenance cost. However, anecdotally benchmarking the experience of clients while designing the workspace maintenance policies is not adequately taken into consideration in India. The focus of this study is on benchmarking workspace usage based on client usability.

The research is descriptive in nature. A structured questionnaire was sent to Information Technology (IT) companies in India to collect data through SurveyMonkey. Stratified sampling was used to collect a sample of 697 respondents which was also verified using G* software. The data collected was analysed using descriptive statistics and partial least square–structured equation modeling (PLS-SEM) to investigate the mediating effect of benchmarking the workspace usage on portfolio optimization and client satisfaction.

The structural model results obtained through the bootstrapping technique show that benchmarking workspace usage for real estate management positively impacts client satisfaction in the Indian IT workspace. The findings of this study support the full mediation effect (97%) and indicate that benchmarking practices are necessary for developing strategies for optimal portfolio asset utilization and are essential to survive in the current competitive business environment.

The findings of this study were influenced by the feedback from the top 100 IT clients in India. The research findings vary according to the cost-benefit analysis of adopting benchmarking measures in small and medium-sized IT companies which still benchmark the workspace usage based on cost-saving measures. Also, very sparse research has been conducted in the workspace management domain of IT firms, so the results of this study can further be used as a reference to explore this area.

The study provides useful insights into how benchmarking in the workspace management domain of the CRE industry can be applied to address portfolio-related challenges, divergent client needs and improve workspace usability following energy-efficient policies. Practitioners can use this study as a guide to develop more effective workspace management policies.

This study may guide other firms to benchmark their current workspace usage and evaluate the impact of their workspace management policies based on the theoretical framework of value-added balanced benchmarking criteria.

This research adds value to the limited literature available on the impact of technology-enabled portfolio optimization techniques through benchmarking which can reduce workspace usage and enhance the usability of the workspace.