Real Estate Technology
Article | July 12, 2022
While many workers plan, at least according to recent surveys, to continue spending at least part of each week working from home, a shorter commute still seems to be holding increasing appeal. The National Association of Home Builders (NAHB ) says its first quarter Home Building Geography Index (HBGI) indicates not only a pandemic driven shift in construction to low density, low cost markets, but a rapid expansion in areas with the shortest commutes.
Litic Murali, writing in NAHB's Eye on Housing blog, says workplaces are moving toward hybrid home/office work models which could affect 30 to 40 percent of the American workforce. This will give renters and buyers increased market power over their travel times and the ability to reduce both housing and transportation costs.
The nationwide average commute is 26 minutes. Those counties in the bottom quintile (lowest 20 percent) have a commute time of 18 minutes or less while the commute in the highest quintile is 28 minutes. The data show that 36.2 percent of the U.S. population resides in the counties in that top slice.
The HBGI indicates that the top two quintiles with the longest commutes together had 63.6 percent of single family building. However, growth was strongest in that bottom quintile with a four-quarter moving average annual growth of 22.2 percent.
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Real Estate Investment, Asset Management
Article | May 5, 2023
The spring selling season might be pushed back for a couple of weeks or even months as lockdowns restrict activity in some states and territories, according to CoreLogic. Prior to the COVID-19 pandemic, sales and listing turnouts typically rise from September to November. Over the ten years to December 2019, the growth in new listings during spring averaged 15.7% while sales hit 6.8%.
CoreLogic head of research Eliza Owen said both sales and listings tend to be most seasonal in the capital cities, particularly in Sydney and the ACT. With the lockdowns, however, the in-demand locations might not witness the same level of activity this upcoming spring, which is only two weeks away. "Observing housing market performance through lockdowns reveals that both sales and listings volumes will fall through lockdowns," Ms Owen said.
What can be learned from last year's Melbourne lockdown?
The extended lockdown in Melbourne last year could provide a glimpse as to what could happen in this year's lockdowns. Melbourne was in lockdown from mid-July to late October. During the period, listings dropped consistently, hitting the lowest at 1,411 in the four weeks to September, which was 80.7% lower than the previous five-year average.
There are several factors that contributed to the slowdown during the period.
Aside from the obvious restrictions that have limited inspections and auctions to virtual sessions, the low levels of consumer confidence also dampened the overall market sentiment, with vendors being unsure whether they would get an optimal price for their properties. Mortgage repayment deferrals and other government support also contributed, as these prevented distressed sales. However, when restrictions in Melbourne got lifted by late October, there was a sudden shift in the market mood, with listings quickly recovering. "New listings volumes through December 2020 trended an average 40.4% higher than the previous five-year average, suggesting the spring selling season of 2020 was 'pushed back' into the final months of the year," Ms Owen said.
Lockdowns to only postpone market activity
Ms Owen said the trend in sales and listings through a lockdown indicate the relative stability of the economy and the housing market amid the COVID-19 pandemic. "This has meant that housing purchasing decisions were more likely to have just been postponed through lockdowns, rather than abandoned all together.” In fact, the muted sales activity through lockdowns actually led to an uplift in sales across Melbourne in December of 2020 and July 2021, a time when seasonally, sales volumes would usually be far more subdued.
"There are tailwinds in place for housing market demand to suggest this may happen again; household savings rates remain elevated, new average mortgage rates continue to reach new record lows, and many government fiscal stimulus and broader institutional responses have been resurrected amid renewed lockdowns," Ms Owen said.
Affordability might become a concern
The consistent surge in prices across capital cities in recent months have already resulted in the inevitable constraints in affordability. CoreLogic's Hedonic Home Value Index in July showed a 1.6% gain in dwelling values, a retreat from the previous growth of 1.9%. Ms Owen said some support schemes that supported consumer sentiment, such as JobKeeper and HomeBuilder have already ended which could dampen the expected rebound in demand.
The rising threat of the Delta variant of COVID-19 might also be a major headwind, as it could result in further lockdowns which will ultimately impact the incomes of Australian households. "With affordability constraints becoming a larger obstacle in the market, as well as the potential for tighter credit conditions further down the track, if buyer activity does not match the lift in listings we could see a gradual rebalancing between sellers and buyers," Ms Owen said.
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Real Estate Technology, Asset Management
Article | June 15, 2023
More than just reducing carbon emissions from building operations will be required to decarbonize cities.
As communities commit to ambitious net zero objectives, the most forward-thinking are increasingly thinking about how to better plan building lifecycles, from construction through maintenance and, eventually, destruction.
The real estate business generates the most garbage in the world. Construction and demolition trash accounts for around one-third of total garbage in the EU and nearly 40% of solid waste in the US.
If cities want to attain a net zero future, this must alter soon. This is why circular economy philosophy, which strives to minimize waste, is gaining traction. It has far-reaching implications for decarbonizing cities because it considers whole-life emissions - the carbon emitted from the production and transportation of goods all the way to their usage and disposal.
However, this is only a portion of the solution. For example, in North America and Europe, almost 80% of buildings that will be in use in 2050 already exist today and will fall significantly short of future carbon reduction objectives. However, demolishing an old structure in order to construct a new, greener structure is not a viable strategy.
Regulations On the Horizon
Incoming rules in large cities are increasingly on developers' minds. By 2030, Amsterdam will have cut its usage of new raw materials in half, on its way to being totally circular by 2050.
Los Angeles aims to be the largest city in the United States to reach zero waste by 2025, with a 90% garbage diversion rate, while Melbourne is likewise on a similar path.
With Design for Reuse Principles, Paris is paving the path for 50% of building projects to send no trash to landfill by 2030. These urge developers to focus on facilities that can support several purposes over time - residences, workplaces, hotels - without requiring large modifications or improvements. By 2030, 30% of its office stock will be required to be flexible.
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Real Estate Advice, Asset Management
Article | May 9, 2023
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.
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