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What Property Investors Need to Know About Sustainable And Impact Investing

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There’s no escaping the importance of sustainability in any investment sector. Globally, Environmental, Social and Governance (ESG) investing is worth $30 trillion in assets under management each year, around a quarter of all professionally-managed assets. It is more relevant than ever as the ‘high impact, low probability’ shock imposed by Covid-19 has strengthened the case for prioritising people and planet alongside profits, and illustrated the power of collective action to tackle global problems.

Many investors are unaware how significant this trend will be. If you are an investor, you need to consider why sustainability will be important, what sustainable property investing actually means, and what the major issues and opportunities are, as these will affect your risks and returns.

Why is sustainability so important for investors?

The UK’s legally-binding commitment to achieve net carbon zero by 2050 means that sustainability is no longer a ‘nice to have’. Our legal obligation is showing up in the form of new rules, regulations and best practices affecting all sectors that contribute to emissions.

40 per cent of UK emissions come from households, which makes the chance of more regulations and policies around the environmental performance of property more likely than not. These regulations will not only affect your ability to operate in a way that is compliant, but fundamentally change the value, performance and risk associated with your investments.

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A Guide to Measure Data-driven Real Estate Asset Management Services

Article | July 21, 2022

Explore the latest trends, tools, and strategies for optimizing data-driven real estate asset management services and achieving long-term success with a comprehensive guide to improving business ROIs. Contents 1 Importance of Data-driven Model for Real Estate Asset Management 2 Seven Steps to Measure Data-driven Asset Management 2.1 Defining the Purpose and Scope 2.2 Identifying the KPI 2.3 Determining the Sources 2.4 Collecting and Cleaning Data 2.5 Data Analysis 2.6 Performance Evaluation 2.7 Continuous Monitoring 3 Effectiveness Metrics for Data-Driven Asset Management 3.1 Occupancy Rate 3.2 Operating Expense Ratio 3.3 Tenant Retention Rate 4 Conclusion 1. Importance of Data-driven Model for Real Estate Asset Management As real estate technology continues to develop and become more cost-effective for both new and existing business structures, and as collaboration platforms, sensors, and smart devices continue to advance, the amount of data produced by commercial real estate assets is growing exponentially. This data can give real estate market participants like investors, asset managers, property managers, and tenants a competitive advantage and help them avoid disruption if they develop data-driven services and new business models centered on the specific needs of users, owners, or the property itself. However, only a concerted effort by all real estate stakeholders including builders, investors, owners, tenants, and service providers towards data-driven real estate asset management can optimize data to generate insights that improve performance and profitability. The significance of data-driven models in real estate asset management is growing as the models enable more informed decision-making and more efficient operations by collecting and analyzing data from various sources. Real estate asset managers can gain a greater understanding of the performance of their assets and make more informed management decisions. This can result in increased efficiency, profitability, and tenant satisfaction. 2. Seven Steps to Measure Data-driven Asset Management Measuring the effectiveness of data-driven real estate asset management services enables businesses to evaluate their current strategies and identify areas for improvement in the services they offer By following these steps to measure asset performance, processes, and activities, businesses can gain insights and make data-driven decisions to optimize performance and maximize returns. 2.1 Defining the Purpose and Scope The process of measuring data-driven real estate asset management services begins with a clear definition of the purpose and scope of the measurement. It involves conducting a comprehensive review of the business goals as well as identifying specific objectives and purposes for the strategies to develop a well-defined purpose and scope for measuring the effectiveness of asset management services. It helps to ensure that the real estate asset management services are aligned with the broader business strategy. 2.2 Identifying the KPIs Defining the purpose and scope of data-driven asset management is followed by identifying KPIs to measure success. It requires a clear understanding of critical areas of asset management and selecting quantifiable measures to define success factors and track progress. Choosing the right KPIs provides valuable insights into asset performance, enabling real estate executives and managers to make informed, data-driven decisions to optimize performance and maximize returns. 2.3 Determining the Sources Identifying the data type, including financial, property, market, and tenant, is essential to determine the sources for evaluating data-driven asset management services. After establishing the data requirements, the sources, such as internal systems and databases, third-party data providers, and publicly accessible data sources, are determined with data compliance and security as the determining factor. Determining sources ensures that the asset management data is trustworthy, current, and accurate, which impacts subsequent decision-making. This step provides the groundwork for data-driven decision-making. 2.4 Collecting and Cleaning Data Data collection and cleansing are essential for measuring data-driven asset management services. The collected data must be precise, exhaustive, and dependable for subsequent analysis and decision-making. The step involves validating the data for completeness and accuracy, eliminating errors, inconsistencies, and duplicates, and standardizing the data across all sources. The process identifies improvement opportunities, optimizing real estate asset management services for maximum efficiency and profitability. 2.5 Data Analysis Data analysis plays a critical role in measuring data-driven asset management services. After finalizing the data collection and cleaning step, the data is analyzed using various techniques such as statistical analysis, predictive modeling, and data visualization. These techniques help to identify trends, patterns, and relationships that provide insights into asset performance. Data analysis provides a more profound understanding of the performance of real estate assets, leading to improved efficiency, increased profitability, and enhanced tenant satisfaction. 2.6 Performance Evaluation Evaluation of data performance to comprehend improvements in the data-driven asset management services starts once the data is analyzed. The performance evaluation step involves comparing actual results to the established KPIs to determine whether the goals are being met or whether there are areas for improvement. It aids in identifying deviations from predetermined objectives and prompts and taking required corrective actions to realign with the business strategy. In addition, this step facilitates identifying improvement opportunities and ensures that real estate asset management services are optimized for maximum efficiency and profitability. 2.7 Continuous Monitoring Measurement of data-driven real estate asset management services ends with continuous monitoring. To ensure asset management strategies are working, continuously tracking and evaluating KPIs from earlier steps while identifying underperformance and improvement opportunities is involved in the last stage. Operation managers can make data-driven choices, identify risks and opportunities, and optimize asset management strategies for efficiency and profitability by monitoring real estate asset performance. In addition, it ensures that real estate asset management services remain effective over time and can adapt to market changes to maintain a competitive edge. 3. Effectiveness Metrics for Data-Driven Asset Management Effectiveness metrics for data-driven asset management services are the KPIs used to measure the success of data-driven strategies. These metrics help real estate executives and managers evaluate the performance of their assets and make data-driven decisions for maximum efficiency and profitability. 3.1 Occupancy Rate The occupancy rate is an essential metric in data-driven real estate asset management. This metric indicates the proportion of a property's rental units that are occupied at present. A higher occupancy rate suggests the property performs well, as more tenants occupy the units. Therefore, this metric can be used by real estate businesses to gauge the efficacy of their digital asset management strategies. 3.2 Operating Expense Ratio The operating expense ratio is used to evaluate a property's operational efficiency for data-driven asset management. It is calculated by dividing the operating expenses incurred by the property by the total rental income generated. It helps measure the proportion of income consumed by the expenses, such as maintenance costs, utilities, and commercial property management fees. A lower operating expense ratio indicates better cost control and efficient use of resources, resulting in increased profitability for the real estate asset. 3.3 Tenant Retention Rate In real estate asset management, the tenant retention rate is an essential metric that measures the proportion of tenants who choose to renew their lease agreements. It is an essential indicator of tenant satisfaction and the quality of property management services. A higher tenant retention rate indicates that tenants are satisfied with the property and management, resulting in a stable tenant base, lower vacancy rates, and decreased costs associated with tenant turnover. 4. Conclusion The significance of utilizing data-driven models for real estate asset management is rising due to digital real estate asset management, technological advancements, and the expansion of collaboration platforms, sensors, and intelligent devices. To gain a competitive edge and avoid disruption, stakeholders in the real estate industry must prioritize the development of data-driven services and innovative business models that cater to the unique needs of users, owners, and the property itself. In addition, the use of data-driven models can also lead to more efficient and informed decision-making, reducing costs along with increasing profits and improving real estate portfolio management.