House prices, airport location proximity, air traffic volume and the COVID-19 effect

Thanh Ngo, Graham Squires, Michael McCord, Daniel Lo

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
168 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)418-438
Number of pages21
JournalRegional Studies, Regional Science
Issue number1
Early online date12 Apr 2023
Publication statusPublished (in print/issue) - 12 Apr 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.


  • house prices
  • airports
  • air-traffic activity
  • cities
  • amenities


Dive into the research topics of 'House prices, airport location proximity, air traffic volume and the COVID-19 effect'. Together they form a unique fingerprint.

Cite this