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This study aims to develop a modelling framework of housing supply dynamics within the context of urban microsimulation systems. Housing markets have witnessed substantial investigation over recent decades, predominantly concerning residential demand. However, comparatively limited attention has been directed towards comprehending the housing supply dynamics. Housing policy disconnects with the developers’ market behaviours, which leads to significant mismatch between the housing construction and affordable housing needs of the population. Research attention should be made in comprehending the residential construction market activities. To address this gap, this study developed an autoregressive distributed lag (ARDL) model and analyzed the temporal evolution of housing construction.

An ARDL model was developed to address the issue of temporal modelling of the housing supply. An empirical study was conducted in the Greater Toronto and Hamilton Area (GTHA) based on a longitudinal housing starts data set from 1998 to 2020. The model integrates diverse variables, including macroeconomic conditions, property development costs, dwelling prices and opportunity costs. Notably, the model captures both the path-dependent effects stemming from supply market fluctuations and the temporal lag effect of influential factors.

The findings reveal that the supply-side’s responsiveness to market condition alterations may span up to 18 months. The model has reasonable and satisfying performance in fitting the observed starts. The methodological foundations laid will facilitate future modelling of housing supply dynamics.

This study innovatively separated the modelling of housing supply within the context of urban microsimulation, into two parts, the modelling of housing starts and completion. The housing starts are determined in a complex and regressive process influenced by both the micro-economic environment and the construction cost and housing market trends. Through the temporal modelling method, this study captures how long it would take for the housing supply to respond to multiple factors and provides insight for urban planners in regulating the housing market and leveraging various policies to influence the housing supply.

The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices.

This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models.

The empirical results show that the ML algorithms achieve good accuracy (R² of 0.873 after cross-validation), and the temporal regression produces competitive results (R² of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type.

Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners.

Describes a weekend workshop on contemporary organizational consultancy, held in April 1992. Themes explored included types and levels of consultation; styles of intervention; consultation as a facilitation, as education, as a political process; changing needs of organizational clients; and competences required of consultants. Highlights the value of time spent in reflective thought rather than in activity. Discusses how The Tavistock Institute in the UK has instituted new courses and seminars designed to cater to practising organizational consultants wanting to enhance their professionalism. Details the content and aims of a new programme which runs from October 1993 to May 1995.

Contemporary organisational realities challenge consultants to develop: more up‐to‐date motivational knowledge; the ability and willingness to work with change agents unsympathetic to applied social science; and, facility with methodologies for both cultural and structural change. Changes over the last two decades have resulted in a more diverse motivational picture than the 1950s to 1970s humanistic motivational theories that underlie most organisational development theory and practice. In response, consultants need to develop a portfolio of consultancy methods that enable them to address not only cultural changes (e.g. attitudinal and change) but also structural changes (e.g. organisational design). Today, most clients who hire organisational consultants do so to complement technological and strategic changes anticipated or already undertaken. OD practitioners need to increase their ability to understand and work with change managers, and their consultants, who see the world predominantly through technological and economic eyes.