"Oft expectation fails": a time-series analysis of construction starts in the London office market

Expectations of future market conditions are generally acknowledged to be crucial for the development decision and hence for shaping the built environment. This empirical study of the Central London office market from 1987 to 2009 tests for evidence of adaptive and naive expectations. Applying VAR models and a recursive OLS regression with one-step forecasts, we find evidence of adaptive and naïve, rather than rational expectations of developers. Although the magnitude of the errors and the length of time lags vary over time and development cycles, the results confirm that developers’ decisions are explained to a large extent by contemporaneous and past conditions in both London submarkets. The corollary of this finding is that developers may be able to generate excess profits by exploiting market inefficiencies but this may be hindered in practice by the long periods necessary for planning and construction of the asset. More generally, the results of this study suggest that real estate cycles are largely generated endogenously rather than being the result of unexpected exogenous shocks.

Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis

This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on microfirms' performance and produce a clear characterization of who benefits from these programs.

Are local economic development incentives promoting job growth? An empirical case study

At a time when cities are competing with one another to attract or retain jobs within a globalizing economy, city governments are providing an array of financial incentives to stimulate job growth and retain existing jobs, particularly in high cost locations. This paper provides the first systematic and comprehensive analysis of datasets on economic development incentives in New York City over the last fifteen years. The evidence on job retention and creation is mixed. Although many companies do not meet their agreed-upon job targets in absolute terms, the evidence suggests that companies receiving subsidies outperform their respective industries in terms of employment growth, that is, the grow more, or decline less. We emphasize that this finding is difficult to interpret, since firms receiving incentives may not be representative of the industry as a whole. In other words, their above-average performance may simply reflect the fact that the Economic Development Corporation (EDC) selects economically promising companies within manufacturing (or other industries) when granting incentives. At the same time, it is also possible that receiving incentives helps these companies to become stronger.

Commercial real estate return distributions: a review of literature and empirical evidence

This paper review the literature on the distribution of commercial real estate returns. There is growing evidence that the assumption of normality in returns is not safe. Distributions are found to be peaked, fat-tailed and, tentatively, skewed. There is some evidence of compound distributions and non-linearity. Public traded real estate assets (such as property company or REIT shares) behave in a fashion more similar to other common stocks. However, as in equity markets, it would be unwise to assume normality uncritically. Empirical evidence for UK real estate markets is obtained by applying distribution fitting routines to IPD Monthly Index data for the aggregate index and selected sub-sectors. It is clear that normality is rejected in most cases. It is often argued that observed differences in real estate returns are a measurement issue resulting from appraiser behaviour. However, unsmoothing the series does not assist in modelling returns. A large proportion of returns are close to zero. This would be characteristic of a thinly-traded market where new information arrives infrequently. Analysis of quarterly data suggests that, over longer trading periods, return distributions may conform more closely to those found in other asset markets. These results have implications for the formulation and implementation of a multi-asset portfolio allocation strategy.

The inter-temporal stability of real estate returns: an empirical investigation

This paper examines one of the central issues in the formulation of a sector/regional real estate portfolio strategy, i.e. whether the means, standard deviations and correlations between the returns are sufficiently stable over time to justify using ex-post measures as proxies of the ex-ante portfolio inputs required for MPT. To investigate these issues this study conducts a number of tests of the inter-temporal stability of the total returns of the 19 sector/regions in the UK of the IPDMI. The results of the analysis reveal that the theoretical gains in sector and or regional diversification, found in previous work, could not have been readily achieved in practice without almost perfect foresight on the part of an investor as means, standard deviations and correlations, varied markedly from period to period.

Dynamic analysis of house price diffusion across Asian financial centres

The aim of this paper is to explore effects of macroeconomic variables on house prices and also, the lead-lag relationships of real estate markets to examine house price diffusion across Asian financial centres. The analysis is based on the Global Vector Auto-Regression (GVAR) model estimated using quarterly data for six Asian financial centres (Hong Kong, Tokyo, Seoul, Singapore, Taipei and Bangkok) from 1991Q1 to 2011Q2. The empirical results indicate that the global economic conditions play significant roles in shaping house price movements across Asian financial centres. In particular, a small open economy that heavily relies on international trade such as – Singapore and Tokyo - shows positive correlations between economy’s openness and house prices, consistent with the Balassa-Samuelson hypothesis in international trade. However, region-specific conditions do play important roles as determinants of house prices, partly due to restrictive housing policies and demand-supply imbalances, as found in Singapore and Bangkok.

Is there a demand for sustainable offices? An analysis of UK business occupier moves (2006–2008)

‘Sustainable’ or ‘green’ commercial buildings are frequently seen as a growth sector in the property investment market. This research examines the emergence of sustainable commercial buildings in both the UK and overseas. The empirical part of the paper is based on a telephone survey of 50 UK corporate (private sector) occupiers taking leased and owner–occupied office space, which was carried out during the period of April to November 2008. The survey focused on actual moves made within the previous two years, or moves that were imminent during 2006–2008. The research suggests that although there is an emerging and increasing demand for sustainable offices in the UK, other factors such as location and availability of stock continue to remain more important than sustainability in determining occupiers’ final choice of office. Occupiers who moved to a Building Research Establishment Environmental Assessment Method (BREEAM)‐rated building, and were in business sectors with strong environmental and corporate responsibility policies, placed more emphasis on sustainability than other groups in the final choice of office, but location and availability remained paramount.

I wouldn’t start from here: some reflections on the analysis of housing markets

The insights of behavioural economics are questioned and an approach suggested that is based on empirical studies of how people actually behave in housing markets.

An analysis of the determinants of cross-border real estate development flows

This paper investigates the scale and drivers of cross-border real estate development in western and central and eastern Europe (CEE). Drawing upon existing literature on the integration of international real estate markets, we make some inferences on expected patterns of cross-border real estate development from this literature review. The paper draws upon a transactions database in order to assess the penetration of national markets by international real estate developers. The determinants of cross-border transaction flows are modeled as a function the range of economic and real estate variables. Whilst western European markets tend to be dominated by local developers, much higher levels of market penetration by international real estate developers are found in the less mature markets of central and eastern Europe. Empirical modelling based on gravity model specifications reveal the importance of size of the economies, distance between countries, extent of globalization and EU membership as significant determinants of cross-border real estate development flow.

Spatial-temporal aspects of cost-benefit analysis for park management: an example from Khao Yai National Park, Thailand

Using a model calibrated to Khao Yai National Park in Thailand, this paper highlights the importance of generating explicitly spatial and temporal data for developing management plans for tropical protected forests. Spatial and temporal cost-benefit analysis should account for the interactions between different land uses – such as the benefits of contiguous areas of preserved land and edge effects – and the realities of villagers living near forests who rely on extracted resources. By taking a temporal perspective, this paper provides a rare empirical assessment of the importance of quasi-option values when determining optimal management plans.

A method for under-sampled ecological network data analysis: plant-pollination as case study

In this paper, we develop a method, termed the Interaction Distribution (ID) method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place on the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The method applies the Dirichlet distribution to estimate these two probabilities, based on a given empirical data set. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.

Empirical test of an agricultural landscape model: the importance of farmer preference for risk aversion and crop complexity

Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns.

Analysis of full-waveform LiDAR data for classification of an orange orchard scene

Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient c was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and c. For single-peak waveforms the scatterplot of c versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return c values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the c versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient c of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties.

Using synoptic type analysis to understand New Zealand climate during the Mid-Holocene

Diagnosing the climate of New Zealand from low-resolution General Circulation Models (GCMs) is notoriously difficult due to the interaction of the complex topography and the Southern Hemisphere (SH) mid-latitude westerly winds. Therefore, methods of downscaling synoptic scale model data for New Zealand are useful to help understand past climate. New Zealand also has a wealth of palaeoclimate-proxy data to which the downscaled model output can be compared, and to provide a qualitative method of assessing the capability of GCMs to represent, in this case, the climate 6000 yr ago in the Mid-Holocene. In this paper, a synoptic weather and climate regime classification system using Empirical Orthogonal Function (EOF) analysis of GCM and reanalysis data was used. The climate regimes are associated with surface air temperature and precipitation anomalies over New Zealand. From the analysis in this study, we find at 6000 BP that increased trough activity in summer and autumn led to increased precipitation, with an increased north-south pressure gradient ("zonal events") in winter and spring leading to drier conditions. Opposing effects of increased (decreased) temperature are also seen in spring (autumn) in the South Island, which are associated with the increased zonal (trough) events; however, the circulation induced changes in temperature are likely to have been of secondary importance to the insolation induced changes. Evidence from the palaeoclimate-proxy data suggests that the Mid-Holocene was characterized by increased westerly wind events in New Zealand, which agrees with the preference for trough and zonal regimes in the models.

EOF analysis of three records of sea-ice concentration spanning the last 30 years

Several continuous observational datasets of Artic sea-ice concentration are currently available that cover the period since the advent of routine satellite observations. We report on a comparison of three sea-ice concentration datasets. These are the National Ice Center charts, and two passive microwave radiometer datasets derived using different approaches: the NASA team and Bootstrap algorithms. Empirical orthogonal function (EOF) analyses were employed to compare modes of variability and their consistency between the datasets. The analysis was motivated by the need for a reliable, realistic sea ice climatology for use in climate model simulations, for which both the variability and absolute values of extent and concentration are important. We found that, while there are significant discrepancies in absolute concentrations, the major modes of variability derived from all records were essentially the same.