Property market modelling and forecasting: simple vs complex models

Purpose – 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. Design/methodology/approach – 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. Findings – 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

Capitalisation: reflections and practice for project appraisal

Income capitalisation is a widely used in commercial property for valuation, development appraisal or for project feasibility analysis. As a decision-making tool, its technical and philosophical limitations are manifest but often overlooked. If bungled, capitalisation, can lead to ‘white elephant’ projects, investment losses or even corporate collapse. To help avoid such waste, the research reviews some technical issues and philosophical conundrums around capitalisation of property investments. Sound capitalisation practice scopes projects, considers capital market, geographic and institutional context and clarifies valuation base(s). Judicious market comparison, risk diagnostics and analytics filter out noise and render complex data to estimate yields or an appropriate discount rate. For a project feasibility analysis, supplementary salient concerns include wider strategic imperatives, indigenous land rights, stakeholder management, administrative probity and the inclusion of environmental or social spillovers

UK REITs don’t like Mondays

Purpose The purpose of this paper is to examine whether Real Estate Investment Trusts (REITs) returns on the different days of the week differ from each other. Design/methodology/approach It uses European Public Real Estate Association (EPRA)/National Association of Real Estate Investment Trusts (NAREIT) UK index daily closing values (GBP) and its two sub-indices FTSE EPRA/NAREIT UK REITs and non-REITs as dependent variables. It employs Kruskal-Wallis tests and dummy-variable regression to test the hypothesis. Findings The overall findings provide evidence that return anomalies exist in the UK REITs. Practical implications Thought significant, the absolute returns differences are modest for investors to gain superior returns in UK REITs. However, by recognising the day-of-the-week effect, investors can buy/sell UK REITs more effectively. Originality/value This research brings updated evidence of the contested calendar anomalies issues in REITs

How long is UK property cycle?

– The purpose of this paper is to assess the duration of the UK commercial property cycles, their volatility and persistence to gauge future market directio

Talent and student private rented sector bottlenecks: a preliminary UK investigation

Purpose – The purpose of this paper is to sketch the UK housing backdrop, review the student private rented sector (PRS) and assess the experience of post-graduate university student tenants in the PRS. Design/methodology/approach – A literature review puts the issues of student-PRS responsiveness into context and helps to untangle some UK housing issues. The private sector’s size, growth and performance is assessed by reviewing secondary data. In-depth interviews were then conducted at a regional university campus. Findings – The study confirms accumulating evidence of an unbalanced UK housing market. The study identified four main PRS issues: first, rapid university expansion without accompanying residential construction has sparked rampant PRS growth with, second, quality issues, third, in tight letting market conditions, rented agent service levels fell and fourth, part of the problem is complex PRS management procedures. Research limitations/implications – The research has three noteworthy limitations. First, the macroeconomic analysis integrated secondary research without independent modelling. Second, the views of letting agents, university property managers, planning officers or landlords were not canvassed. Finally, the pilot interviews were geographically restricted. Practical implications – When they expand, universities, local authorities and industry players need to give due consideration to plan for, design and develop quality student accommodation. Over-reliance on the PRS without informed oversight and coordination could undermine student experience and erode long-term UK competitiveness. Social implications – The lack of quality student rented accommodation mirrors a general housing malaise around affordability, polarisation and sustainable “dwelling”. Standards and professionalism in the rented sector is part of the overall quality mix to attract global talent. Originality/value – The preliminary investigation uses mixed-methods to investigate PRS service delivery. It illustrates the interplay between professional property management and wider issues of metropolitan productivity, sustainability and resilience

ARIMA modelling of Lithuanian house price index

Purpose – This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies. Design/methodology/approach – The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context. Findings – As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes. Research limitations/implications – Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market. Originality/value – Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies

Two centuries of farmland prices in England

The dissemination of robust asset price data can help improve market efficiency, resource allocation and investment analysis. Land prices influence housing affordability, food security and the carbon infrastructure. Yet price and return histories for farmland in England are fragmented. To provide perspective, a long farmland price series is needed to improve transparency and bring the asset class into line with commercial and residential real estate. After reviewing the historical backdrop and considering methodology, this research uses a chain-linking approach to construct a long-term farmland price series for England. It then adjusts the series for inflation to examine real land prices. The resulting two-century English farmland prices series contributes to farmland market analysis. Notwithstanding some concerns with long-run chain component heterogeneity, the combined series helps us to understand English average farmland price dynamics. As measured by the geometric mean, English land price real capital returns have been positive over more than two centuries. Farmland real price growth was 0.33 per cent annually from 1781 to 2013 and 0.71 per cent from 1801 to 2013. The series contributes to an understanding of land price dynamics. Sir, – In these times, when the rental and marketable value of lan