Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

• Date: 29 October 2024
• Pages: 44-75
• DOI: https://doi.org/10.1108/IJHMA-08-2024-0117
• Published date: 29 October 2024
• Subject Matter: Property management & built environment,Real estate & property,Housing markets
• Author: Emeka Austin Ndaguba,Cina Van Zyl

Data-driven approach to optimizing

property management strategies:

spatial modeling analytics of

short-term rentals

Emeka Austin Ndaguba and Cina Van Zyl

Department of Applied Management, University of South Africa, Pretoria, South Africa

Abstract

Purpose –This study aims to explore the impact of locational and seasonal factors on the f‌inancial performance

of short-term rental properties in Margaret River,Western Australia. It seeks to address the gap in understanding

how these factors inf‌luence key f‌inancial metrics such as average daily rate (ADR) and occupancy rates, providing

insights for property managers, inves tors and policymakers.

Design/methodology/approach –The research uses a mixed-method approach, integrating advanced

predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering

algorithms like density-basedspatial clustering of applications with noise (DBSCAN)and ordering points to

identify the clustering structure(OPTICS). The study analyzes a comprehensive data set ofshort-term rental

properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and f‌inancial

outcomes.

Findings –The f‌indings reveal that properties located near tourist attractions and amenities

consistently achieve higher ADRs and occupancy rates, conf‌irming the critical role of location in

driving rental demand. Seasonal analysis indicates signif‌icant f‌luctuations in both ADR and occupancy

rates, with peaks during high tourist seasons and troughs in off-peak pe riods. The study underscores the

importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different

seasons. In addition, it highlights the inf‌luence of property features, such as the number of bedrooms

and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher

occupancy rates.

Research limitations/implications –Future research could expand the scope to include different

locations and explore the long-term impacts of locational and seasonal factors on property

performance.

Originality/value –This research contributesto the literature by integrating spatial analysis with advanced

predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors

impact f‌inancial performance in the short-term rental market. It offers a novel application of data analytics

© Emeka Austin Ndaguba and Cina VanZyl. Published by Emerald Publishing Limited. This article is

published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce,

distribute, translate and create derivative works of this article (for both commercial and non-

commercial purposes), subject to full attribution to the original publication and authors. The full terms

of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Author contributions: Conceptualisation, E.A.N. and C.V.Z.; methodology,E.A.N.; software, E.A.

N.; validation, E.A.N.; formal analysis, E.A.N. and C.V.Z.; investigation, E.A.N. and C.V.Z.;

resources, E.A.N.; data curation, E.A.N. and C.V.Z.; writing −original draft preparation, E.A.N.;

writing −review and editing, E.A.N. and C.V.Z.; visualisation, E.A.N.; supervision, C.V.Z.; project

administration, E.A.N. All authors have read and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Conf‌licts of interest: The authors declare no conf‌licts of interest.

IJHMA

17,7

44

Received16 August 2024

Revised21 September 2024

Accepted25 September 2024

InternationalJournal of Housing

Marketsand Analysis

Vol.17 No. 7, 2024

pp. 44-75

EmeraldPublishing Limited

1753-8270

DOI 10.1108/IJHMA-08-2024-0117

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/1753-8270.htm

within the context of tourism and hospitality management, bridging theoretical frameworks with practical

insights.

Keywords Property management, Dynamic pricing, Location theory

Paper type Research paper

1. Introduction

This study focuses on the short-term rental market, which has emerged as a signif‌icant

component in global tourism, driven by platforms like Airbnb, Stayz and Explora for

generating extra income and the increasing demand for f‌lexible accommodation options

(Gutiérrez et al., 2017a;Ndaguba and Van Zyl, 2024). In highly frequented tourist

destinations such as Margaret River, Western Australia, the success of short-term rental

properties is intricately linked to their geographical location and the seasonal patterns of

tourism (Dogru et al., 2019a;Shoval et al., 2020a). Understanding the inf‌luence of location

on rental property success is crucialfor effective property management strategies. Locational

factors, such as proximity to touristattractions and infrastructure, are pivotal in determining

the f‌inancial performance of short-term rentalproperties (Gunter and Önder, 2018a;Xie and

Kwok, 2017a;Molotch, 1976). Despite the recognized importance of these factors, there

remains a gap in the literature concerning the comprehensive impactof spatial and temporal

dynamics on the f‌inancial performance of short-term rentals (Abrate and Viglia, 2012;

Koenig-Lewis and Bischoff, 2010a). In that, the studies argue that seasonal f‌luctuations

signif‌icantly impact the f‌inancial performance of short-term rental properties, necessitating

adaptive pricing models (Ndagubaand Brown, 2024). Because seasonality tend to introduce

signif‌icant variability in occupancy rates and price, highlighting the need for dynamic and

responsive pricing strategies to maximize revenue (Chung and Law, 2020a;Dogru et al.,

2019a). Molotch’s (1976) study was instrumental in theorizing location, tourism

development and the role of urban growth policies. This is helpful in discussing how spatial

factors inf‌luence short-term rental markets. Molotch’s theory is especially relevant when

analyzing how urban growth and infrastructuredevelopment inf‌luence the attractiveness and

prof‌itability of short-term rentals. Relph’s (1976) research demonstrates how attachment to

place and spaces are perceived as “places”of signif‌icance enhances the exploration of

geographical signif‌icanceof rentals near tourist attractions.

This research addressesthis gap by investigating how locational attributes and seasonality

affect key performance indicators such as price and occupancy rates (Molotch, 1976). To

achieve this objective, the study adopted several advanced data analytical model, including

clustering algorithms and predictive modeling, which are powerful tools for optimizing

occupancy rates and price. By usingthese techniques, clustering algorithms such as density-

based spatial clustering of applications with noise (DBSCAN) and predictive modeling

techniques such as Random Forests were used in developing a robust framework for

understanding and optimizingproperty management strategies (Li et al., 2020;Shoval et al.,

2020a). Thus, the application of spatial modeling and advanced predictive techniques,

enhances the understanding of market dynamics and supports more precise strategic

decisions (e Silva et al.,2018;Li and Law, 2020). Furthermore, the location theory was

integrated with revenue managementtheory, which offers a novel approach to analyzing the

short-term rental market, provided actionable insights for property managers, investors and

policymakers. The theoretical contribution of this research is the integration of location

theory and revenue management theory to provide a comprehensive framework for

analyzing short-term rentalmarkets. The fusion of location theory and revenue management

theory provides a robust analytical framework largely unexplored. The f‌indings from this

International

Journal of

Housing Markets

and Analysis

45

study provides both practical highlight and policy recommendations, by showcasing the

importance of location and seasonality in shaping the economic outcomesand operability of

short-term rentals. Properties situated near tourist attractions and well-developed

infrastructure consistently achieve higher average daily rates (ADRs) and occupancy rates,

conf‌irming the signif‌icance of spatialfactors (Dogru et al., 2019b;Xie and Kwok, 2017a). In

addition, the study underscores the necessity of dynamic pricing models that could be

adapted to leverage seasonal demand f‌luctuations, thereby optimizing revenue throughout

the year (Li et al., 2020;Shovalet al., 2020a).

The paper primarily investigated short-term trends and its focus is on spatial and temporal

dynamics of the sector, particularly gauging how locational factors and seasonality affect

f‌inancial performance. While long-term trends might provide broader insights, the paper’sscope

is limited to optimizing property management strategies through real-time analytics, which

necessitates a focus on shorter timeframes. It also uses advanced clustering techniques and

predictive models that are more suited for analyzing immediate market d ynamics. The absence of

a long-term perspective could be a limitation that futur e research might address to build a more

holistic understanding of market trends. This resear ch presents actionable insights for property

managers, investors and policymakers, enabling data-driven decision-making in the competitive

short-term rental market. This study contributes to the broader discourse on property

management by offering a nuanced analysis of the interplay between spatial and temporal factors

in short-term rental markets, with direct implications for stakeholders (Li and Law, 2020;Gunter

and Önder, 2018a). Moreover, it adds a new layer to Harvey’s (1973,2006) works on the spati al-

temporal dynamics of capital and how location and seasonality drive f‌inancial performance in the

short-term rental market. This research signif‌icantly advances the academic understanding of

short-term rental dynamics by investigating the intricate relationships between seasonality, spatial

clustering and the f‌inancial performance of short-term rental properties in Margaret River,

WesternAustralia, specif‌ically examining their effects on ADR and occupancy rates.

The study distinguishes itself from existing research conducted between 2023 and 2024 by

offering a localized analysis of the short-term rental market in Margaret River, WesternAustralia.

Unlike broader studies that explore larger regional markets, such as those by Ndaguba and Van

Zyl (2024) and Li and Fang (2022), this research provides a detailed micro-level spatial analysis,

using advanced clustering techniques (DBSCAN) to identify high-demand areas and assess the

impacts of location on ADR and occupancy rates. It further inn ovates by integrating machine

learning models, such as Random Forests and Gradient Boosting, with seasonality trends to

optimize pricing strategies in real-time. This approach contrasts with previous studies, which rely

more heavily on traditional statistical forecasting (Chung and Law, 2020b). In addition, this

research provides detailed insights into how specif‌ic property features inf‌luence performance

metrics, offering practical tools for property managers. Following the introduction, the paper

progresses through four key phases. The second numbered section presents the theoretical

framework, where location theory is integra ted with tourism and hospitality revenue

management to explain the inf‌luence of location on a property’sf‌inancial pe rformance. The third

and fourth numbered section, while the third addressesthe methodology, outlining how data from

2012 to 2019 was processed and analyzed using a combination of analytical techniques, the

fourth numbered section, displayed results, detailing the outcome derived from the data and

highlights the key insights. Finally,the fourth numbered section, concludes with a discussion and

conclusion, interpreting the f‌indings and providing implications for future studies in the f‌ield.

2. Theoretical framework

This inquiry explores the effects of seasonality and spatial clustering on the ADR and

occupancy metrics of short-term rental properties in Margaret River, Western Australia.

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