Although house prices and airports are influenced by distinct factors that shape their evolutions, they are also intrinsically connected through the natural and built environment. Standard theory suggests that air-traffic noise and proximity to key economic hubs such as airports are of prime importance to house prices and the housing market. This study contributes to understanding the link between the housing market, airport location proximity and air traffic. The research investigates this association across four key urban areas within New Zealand proximal to an international airport: Auckland, Wellington, Christchurch and Queenstown. Applying a generalized least squares (GLS) regression approach, the analysis reveals that house prices, air-traffic activity and proximity to airports within New Zealand demonstrate a statistically significant effect, and that air traffic volume has a positive effect on house prices. Moreover, the findings reveal a ‘U’-shape relationship between distance to the airport and house prices, suggesting that airport noise and pollution adversely affect house prices, with this effect diminishing with distance, indicating that economic influences and employment may also serve as a positive externality.

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity.

Purpose: House price-to-rent (P-t-R) ratios are among the most widely used measures of housing market conditions. Given the theoretical and apparent bidirectional, causal relationships and imbalances between the housing market, broader economy and financial market determinants, it is important to understand the relationship between macro- and micro-economic characteristics in relation to the P-t-R ratio to enhance the understanding of housing market dynamics. This paper studies the joint dynamics and persistence of house prices and rents and examines the temporal interactions of the P-t-R ratio and economic and financial determinants.Design/methodology/approach: The authors examine the lead–lag relationships between the P-t-R ratios and a spectrum of macroeconomic variables using cointegration and causality methods, initially at the aggregate position and also across housing types within the Northern Ireland housing market to establish whether there are subtle differences in how various housing segments react to changes in economic activity and market fundamentals.Findings: The findings reveal price switching dynamics and some very distinct long- and short-run relationships between macroeconomic and financial indicators and the P-t-R ratios across the various housing segments. The results exhibit monetary supply, foreign exchange markets and the stock market to be important drivers of the P-t-R ratio, with P-t-R movements seemingly tied, or are in tandem, with the overall economy, particularly with the construction sector.Practical implications: The study shows that the P-t-R ratio can be used as an early measure for establishing the effects of macroprudential policy changes and how these may manifest across housing tiers and quality, which can further act as a signal for preventing or at least mitigating future irrational price cyclicity. These insights serve to inform housing and economic policy and macroprudential policy design, principally within lending policy and the effect of regulatory interventions and further enhance the understanding of the P-t-R ratio on housing market structure and dynamics.Originality/value: This study is the first in the housing literature that examines the causal relationships between the P-t-R ratio and macroeconomic activity at the sub-market level. It investigates whether and how money supply, inflation, foreign exchange markets, general economic productivity and other important macroeconomic factors interact with the pricing of different property types over time.

Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A.

The price-to-rent (PtR) ratio is one of the most commonly used indicators to assess housing market conditions by policy makers and real estate practitioners. It is often employed as an economic barometer to detect whether a housing bubble exists and determine whether the property market has become unaffordable relative to historical trends. Despite a plethora of research studies on the PtR ratio in the housing literature, relatively little is known about its long-term dynamics with macroeconomic and financial determinants. By utilising time series data on the Hong Kong residential property market, this study examines the cointegration and causal relationships between a wide spectrum of macroeconomic indicators and the PtR ratios of housing segments of different tiers which comprise different socioeconomic groups of homebuyers and investors. The results point towards market compartmentalisation, in the sense that the PtR ratios of the housing submarkets respond to changes in macroeconomic fundamentals in a differential manner. For instance, the PtR ratios of housing segments with a greater proportion of owner-occupiers are statistically less y correlated with investment-related macroeconomic attributes, such as foreign direct investment and equity market performance. On the other hand, the pricing of large-sized housing units in prime locations, generally favoured by investors from mainland China, are found to be Granger-caused by the exchange rate of the Chinese Yuan to the Hong Kong dollar.

Pricing of direct industrial real estate (DIRE) has long been under-researched due to the paucity of analysable data. Compared to other types of real estate, DIRE has often been regarded as more inefficient because of information asymmetry amongst market players stemming from a lack of market transparency. Therefore, pricing of DIRE usually does not follow a random walk and should be more predictable than other types of real estate. Along this line of reasoning, this study empirically investigates the causal relationships between the price-to-rent ratio of DIRE and macroeconomic attributes using cointegration and causality techniques. More specifically, we employ data on the market of Hong Kong to investigate the lead-lag relationships between the price-to-rent ratio of DIRE and a wide spectrum of macroeconomic and financial indicators, including inflation, money supply, national income, exchange rates, performance of housing market and other economic indicators specific to the industrial sector. The results of our statistical tests reveal significant evidence that DIRE is generally moving in syncs with other segments of the economy over time in terms of long-term cointegration. Further, DIRE tends to lag behind the overall macroeconomy in terms of Granger causation with the price-to-rent ratio exhibiting varying lengths of time lag with the macroeconomic determinants. The findings of the study carry important implications for informing property valuation practices and industrial land policy, particularly in designing urban revitalization programmes aimed at optimising industrial land use.