Salient Features of a Robust Real Estate Property Management System ()
1. Introduction
The invention of information technology has significantly reshaped very many industries globally. According to Mills (2013), different types of information technologies adopted in various industries consume more than ten percent of the total global electricity. A greater percentage of activities in many industries now rely on the use of information technology, academia being one of the industries that has an information technology adoption rate of more than 67% according to 2013 statistics (Dahlstrom et al., 2013).
Adoption of information technology in real estate property management can be traced back to early 2000 when it first penetrated into the construction industry. During this particular duration, it was first referred to as “Tipping Point” (Brandon et al., 2005). However, the adoption of information technology has expanded out of the construction industry to cover other related sectors such as drawing and real estate property management. Information technology has also transformed the real estate industry from a very rigid traditional system to modern digital technologies.
According to Stokes (2020), the work of a contemporary real estate manager has significantly been transformed as a result of many changes that have taken place in the last decade. He further asserts that contemporary property managers have greater duties and responsibilities that go beyond the traditional known chores of collecting rent and settlement of bills. Stokes also argues property managers have to organize for insurance, handle issues related to tax, ensure designing and collection of property renovation bids, ensure proper coordination of maintenance services, ensure property security and ensure proper customer relationship management among others.
In order for the above duties and responsibilities to be performed effectively, various technologies have been adopted by real estate managers. These technologies include property management software which have registered a significant proliferation over the last one and half decades. These kinds of property management software have made it easier for property managers to collect and analyze data on various real estate issues thus making real-time decisions. These kinds of real estate property management software have made it possible to provide customer-tailored services and maintain better relationships (Stokes, 2020).
As industry evidence reveals that there is expanded adoption of e-commerce in real estate property management, there is still a lot that requires further investigation in order to provide more knowledge on the subject. This article seeks to identify salient features of a property management e-commerce platform, investigate the drivers of e-commerce adoption and establish the models adopted in optimum pricing of property management systems.
2. Literature Review
In many countries around the globe, there has been an increase in the adoption of e-commerce in many industries including real estate. This trend has been witnessed in countries such as the United States of America (USA), China, Malaysia, South Africa, and Singapore among others. There is also evidence of studies being conducted in the above countries relating to adoption of e-commerce in real estate property management (Zhang et al., 2016). In this section, the paper focuses on a review of various studies conducted on salient features of a robust real estate property management system.
2.1. Salient Features of a Property Management E-Commerce Platform
Puķīte and Geipele (2015) while examining the theoretical aspect of a residential property management system indicated that the system must have five important elements which include legal norms, IT, the technical condition, finance accessibility and psychological preparedness. According to Vanags (2010) there are various laws that govern property management in different countries around the world. He further argues that a robust real estate property management system must take into account the laws that govern property. The system must not only capture the interests of the property owners but also the welfare of the society since a healthy and safe living environment is inscribed in the laws that govern property in most countries.
Masis et al. (2017) also confirm that Information communications Technology (ICT) is a very significant element of a robust real estate property management system. The real estate industry relies heavily on information. Various customers search for relevant information relating to buying, selling or even renting from the relevant sources such as real estate agents and other professionals. The developments in ICT such as internet enabled activities have made it possible for creation of a transparent market place where buyers and sellers can easily be able to interact. This makes it possible and easier for conducting internet based real estate transactions (Richardson & Zumpano, 2012).
Collins (2021) indicates enumerates a total of ten most important features that an ideal property management system must have. The first among these features is a comprehensive accounting module that has been tailored for the industry. Collins further argues that property managers need accurate and reliable accounting information. The accounting module makes it easier for rent payments to be made and accounting entries to be captured. It also reduces the time taken to conduct these activities. The whole essence of having a good accounting module in the property management system is to produce valid reports for decision making.
Property management involves performance of some repetitive tasks. Automating property management is an important feature of a robust property management system since it removes manual performance of the repetitive tasks and also eliminates the possibility of committing errors and loss of paper documents (Collins, 2021). Online rent payment is also another important feature that a property management system has to contain. Allowing the tenants to take control of rent payment eliminates late and non-payment problems. It also saves time and cost on the part of the owner (Collins, 2021).
According to Jianliang and Jianggyin (2012) an ideal real estate property management system should be one that provides a complete Business-to-Consumer e-commerce. They further assert that for this to be achieved, the system needs to have essential modules such as repair and maintenance, transportation security supplier, paid services module, green sanitation module, capability to connect with Enterprise Resource Planning (ERP) system and a toll system. Jianliang and Jianggyin (2012) also indicated that at the core of a real estate property management system is a property services centre that synchronizes all the other modules.
2.2. Drivers of E-Commerce in Real Estate Industry
Ghaith et al. (2018) indicate that the drivers for adoption of e-commerce in the real estate industry can best be analyzed through the Technology Acceptance Model 3 (TAM3). Venkatesh and Bala (2008) while elaborating on TAM3 defined it as a model that presents the various forces that determine adoption of different types of information technologies in an organization. Key drivers proposed by TAM3 include Technology’s perceived ease of use, Cognitive instrumental process variables, anchor variables, adjustment variables and perceived usefulness of the technology.
2.2.1. Perceived Ease of Use
According to Al-Gahtani (2014) for any technology to be adopted and accepted by an organization, the perception on ease of use by the users plays a key role. He further argues that when the users of the proposed technology consider the same as one that is easier to use, they are likely to concentrate on usage of the technology. Ease of use of technology such as e-commerce platforms in real estate industry and the level of enjoyment the users derive from usage of the technology determines its adoption and implementation.
2.2.2. Cognitive Instrumental Process Variables
Venkatesh and Davis (2000) define cognitive instrumental process as a concept that focuses on relevance to the job being done, the service quality and demonstration of results. Adoption of an e-commerce platform will depend on the perception of the users with regard to its suitability in performance of tasks. Various users have different tasks to be carried out and the suitability of any information technology in performing these tasks will facilitate its quick adoption (Porter & Donthu, 2006). Any information technology (including real estate property management e-commerce) must be perceived as one that is capable of improving service delivery levels and leading to better results otherwise resistance to its implementation will be witnessed (Chuttur, 2009).
2.2.3. Perceived Usefulness
Ahmad et al. (2015) argue that the extent to which business owners and the management perceive technology as being useful is what mainly determines its adoption. While supporting this position Calderwood and Freathy (2014) also asserted that some managers and owners of businesses perceive e-commerce as innovative technology that should form part of their business, while others consider it as a critical online selling tool that can significantly improve the revenues of their business.
Still on perceived usefulness as a driver of e-commerce adoption in the real estate industry, Al-Qirim (2007) was of the opinion that the perception on usefulness of technology was dependent on the duration a firm had been in estate. Al-Qirim (2007) further opines that there is a inverse relationship between the age of a firm and level of success in implementation of new technologies existence. He argues that older firms that have been in the market for a long period of time have greater experience and are highly likely to adopt new technologies such as e-commerce for real
2.2.4. Anchor Variables
Al-Gahtani (2014) has indicated that there are four anchor variables that will determine the adoption of e-commerce technology in an industry. He lists these variables as the perception on level of external control of the system, computer self-efficacy, playfulness with computers and anxiety associated with computer use. In implementing e-commerce in real estate, the perception of the users on their ability to use computers in carrying out various functions. When users have a conviction that they can be able to use e-commerce, its implementation becomes easier. However, Surendran (2012) raises a concern that e-commerce technologies are still relatively new in most developing and underdeveloped countries thus making it difficult for users to possess the necessary hands-on experience for adjustment and acceptance of new e-commerce platforms.
2.2.5. Social Influence
The culture that an organization adopts will determine whether it appreciate and implements e-commerce technology. An organization that has a culture of motivating employees towards the use of new technologies in improving services and further supports training on new technologies is likely to adopt new e-commerce technology faster than others (Calderwood & Freathy, 2014).
Other social factors that determine adoption of e-commerce technologies may include the level of computer literacy among the target users, the level of mistrust on use of e-commerce and security issues arising from the use of e-commerce systems (Wu & Hisa, 2008). Supporting this position Ahmad et al. (2015) also assert that the ability of an organization to provide sufficient confidentiality and privacy of customer information is key to adoption of e-commerce technology. Lastly, the owners of a business may not have enough time to commit towards implementation of e-commerce technologies and this will hinder their adoption.
2.3. Pricing Models Adopted by in E-Commerce Platforms
Commercial markets are now more complicated than ever before. There is greater competition among firms thus leading to introduction of newer products into the market and making continuous improvements in order to retain customers and protect the market share. On the other hand, consumers have also become more informed and literate. They have the ability to access important pricing information through the internet using the various e-commerce platforms that are available online. It is no secret that consumers can take a very short time to compare the prices in both some physical traditional stores and those offered in e-commerce platforms online (Faehnle & Guidolin, 2021).
It is therefore important for online retailing and forecasting to be significantly agile and responsive to the real time information originating from the market and include the same into the firm’s pricing strategies. Several researchers have advocated for the use of time series pricing models for online businesses since a significant portion of the data normally comes from the market in form of time series (Takada & Bass, 1998; Franses, 2006; Hsu et al., 2008). Faehnle and Guidolin (2021) therefore advocate for dynamic pricing models for e-commerce businesses.
According to Hajjar (2021) dynamic pricing involves adjustment of the prices depending on data from customers and the other competitors in the market. The author cites major e-commerce platforms such as eBay and Amazon which leverage a lot on dynamic pricing in order to drive higher customer traffic to their sites and maximize profits. Hajjar (2021) argues that dynamic pricing is important in e-commerce for three reasons. The first reason is to pull more customers into the site, the second is to increase sales and the third is to assist customers understand the level of demand for a particular product or service.
Dynamic pricing begins at the point where an e-commerce platform collects market data (Faehnle & Guidolin, 2021). This is normally done through a module that is contained in the e-commerce platform such as the Amazon Price Checker (Hajjar, 2021). Once the data has been collected, the best dynamic pricing module is selected depending on the product under focus. After this the right type of dynamic pricing is selected, the right dynamic pricing algorithms are then applied on the collected data and finally updating and optimization of data takes place (Faehnle & Guidolin, 2021). For example, it is estimated that Amazon changes its prices more than 2.5 million times a day to set them lower than other competitors. This pricing strategy contributed significantly to towards Amazon’s 25% increment in profitability for the year 2016 (Hajjar, 2021).
3. Methodology
The paper systematically reviewed various articles on real estate and technological applications in the sector. While reviewing literature, the researcher relied on online search engines such as Google Scholar, Emerald Insight and Science Direct (see Table 1). In doing this, keyword and semantic searches were applied in attaining the required literature. The study approach involved use of keyword searches, screenings and categorisation (Ullah, Sepasgozar, & Wang, 2018). The paper used keyword searches such as “e-commerce in real estate”, “e-platform in real estate, “Adoption of e-commerce in real estate” and “management of real estate using e-commerce” (see Table 1).
In this paper, the researcher, with the help of a research assistant conducted a thorough literature review for a period of two months. This is in line with Parahoo (2006) who argued that when conducting systematic review, the researcher should indicate the time frame as well as the methods used in evaluating the findings of the study. In ensuring dependability and validity of the review process, the researcher applied the following criteria: clearly formulated research questions; set a clear inclusion and exclusion criteria; selected and accessed the required literature for the study and analyzed, synthesized and disseminated the research findings (Ryan et al., 2007).
While conducting the initial search, a total of 185,780 articles that matched the selection criteria were found. Google Scholar yielded the largest number of articles followed by Direct Science and emerald insight in that order. These were captured as emerald insight 2490, Google Scholar had 163,606 and Direct Science had 19,684 articles as captured in Table 1. The articles that were initially found were subjected to thorough screening and filtering. This was a two-step process that entailed first and second screening and filtering. The process yielded 24 emerald insight, 60 Google Scholar and 15 Direct Science articles respectively. The process focused on relevance, appropriateness and year of publication of the articles to either include or exclude the article to be reviewed. Further, the articles that lacked the keywords in the title or abstract and those that were not written in English were eliminated from the list of articles needed for review. Table 1 presents a summary of the methodology applied in the selection of the articles for review.
4. Findings
4.1. Technological Innovations in Real Estate Sector
Evidence from the reviewed literature indicate that most of the real estate players relies on technological innovations such as internet, dynamic websites and e-commerce platforms with qualities such as clarity of information, service quality, capability of identifying property using features or address and perceived ease of use. Table 2 provides a summary of the findings from various authors.
4.2. E-Platform Features and Value Proposition in Real Estate Sector
In a competitive business environment, especially in the real estate space which has diverse players, business enterprises involved in online platforms need to strategically align key aspects such as purpose of each of the online platforms
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Table 1. Methodology used in filtering reviewed literature.
in the real estate companies e-platform features, value proposition by the online platforms and their target customers. Empirical evidence obtained from various studies provides almost similar features that researchers consider as part of a robust e-commerce real estate property management system. Table 3 presents various online platforms with the mentioned aspects.
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Table 2. Technological innovations in real estate sector.
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Table 3. E-platform features and value proposition.
Source: Adopted from Ullah, Sepasgozar, and Wang (2018).
4.3. Drivers of Adopting E-Commerce in Real Estate Property Management
Empirical evidence from various studies conducted reveals that there are a number of drivers that determine the adoption of e-commerce platforms. The results have been tabulated in Table 4 below.
4.4. Approaches to Pricing in Real Estate Property Management System
Empirical evidence reveals that there are various approaches to pricing models that can be used in business. However, pricing in e-commerce platforms tends to be slightly different since it relies heavily on market data and information from customers. The applicable pricing models must rely on time series since the data comes in time series. A dynamic pricing approach based on time series is recommended. The summary of findings is presented in Table 5 below.
4.5. E-Platform Business Model
The adoption of e-commerce in real estate sector has led to reduce transaction cost, improved speed up circulation process, restructure business process and improved service delivery (Aihua, 2010). The key success parameters in e-platform business model include: how to increase revenue streams, applicable pricing models adopted by online real estate companies cost structure of the platforms. Table 6 presents various platforms with their respective business models.
Source: Adopted from Ullah, Sepasgozar, and Wang (2018).
5. Recommendations
While designing property listing website to serve agents in the real estate, there is need for a different approach in the design of real estate websites. This implies that there is need to provide tools to enable these agents run their business on the platform. This will ensure competitiveness of the platform as compared to the market leaders.
The concept of targeting specific markets using independent websites for each market seems to be well entrenched in the Real Estate sector. There is need to undertake more research to understand why this works better and if there are any big websites having multi-market (many countries) real estate listings on their websites.
6. Limitations of the Study
This study did not focus on big websites with multi-market real estate listings on their websites. Therefore, the findings of this study will not be directly applicable to them.
This study was focusing on the salient features of real estate e-commerce platforms and not all e-commerce platforms. The findings are therefore industry specific hence may not be applicable to other industries.
7. Conclusion
The research indicates that the real estate market is dominated by licensed real estate agents who are the main clients of most real estate websites. These websites mainly have a listing module that links the property buyers and property owners or agents. Most buyers visit real estate websites to get in touch with registered agents in their areas as well as to look for properties they might be involved in. The main deficiency in this article is the fact that it did not include big websites with multi-market real estate listings, an issue that future research work may consider exploring.