Throughout my years in the Commercial/Investment Real Estate (IRE) sector, one thing I have never heard is, “Now that we have reached $XBIL in AUM, there is no need to worry about future growth.” The goal in IRE is always continual growth through a mix of lower risk, “Core” fund offerings (Class A buildings with long term, solvent tenants) and higher risk, opportunistic strategies (redevelop, repurpose, renovate).
With the focus at the start-up of a new IRE firm being heavily weighted toward fast, targeted acquisitions while only incurring bare minimum expenses, a long term, robust IT strategy is rarely even a blip on the radar at this point. Generally, consideration is limited to general network, desktop, and cyber security needs. The flexibility and power offered by Microsoft Excel alone meets the majority of all business solution requirements in the early months and years.
But as many successful shops have learned, by the time they stabilize, come up for air, and take a breath, their operation has long exceeded the capabilities of utilizing Excel as the core of the IT ecosystem. The “lookback” then illustrates quite a variance from the initial IT budget projections.
So, in the era of the digital age, how do firms establish an IT strategy early on, not only to support, but empower long term growth while controlling headcount to AUM ratios?
Aside from the general areas of IT described above, the earlier IRE firms incorporate standardized processes and metrics into their operational cadence the smoother the transition from tools such as Excel to more robust, specialized business solutions. Areas IRE firms should incorporate into their early strategy should include:
1. Consistent capture and storage of acquisition details with specific variables related to property type (Office, Multifamily, Industrial, etc) and Investment Strategy
2. Property/Investment Level Accounting:
a. Alignment with property managers regarding revenue and expenses and how these are reported
b. Clearly defined timelines for submitting financials to support timely asset and portfolio level reporting
3. How many times is the same entered into separate systems? How and where can integration reduce the number of touchpoints?
4. Are the variances in your business processes between your Core and Opportunity Funds clearly defined and does your IT strategy support the needs of both?
Without a clearly defined IT strategy that aligns with your projected growth, any of the factors above will lead to unnecessary costs in terms of infrastructure, licensing, headcount, and redundant business processes. A solid IT strategy should allow your AUM to grow without a direct correlation to headcount.
We at Saxony Partners believe in the mantra of providing the right solutions and technology for our clients. We let the data tell how a product or process should be created or improved. And we do so with a passion for getting our clients and their customers what they need at the appropriate time for it. When you partner with us, you partner for success.
Contact us at (214) 389-7903 or email@example.com to learn how we can help you.
About The Author
Director of Application Development
“This is your life and its ending one moment at a time.”- Chuck Palahniuk
Pete is a program manager within Consulting Services at Saxony Partners. Saxony Partners provides technology, support, and consulting services for clients in the Real Estate/Financial Services industry.
Pete enjoys working here because it is a chance to work directly with a team of knowledgeable and well-respected colleagues that he has crossed paths with in the past.
The Real Estate industry contributes close to three trillion dollars to the total US GDP. That share continues to grow each year as is indicated by the graph below. While real estate investments can range from low risk/low return to high risk/high return, investors should anticipate a long investment cycle. Due to the volatility in the real estate market, particularly after the 2008 financial collapse it has become critical to minimize the real estate investment losses. Investors need to carefully research the real estate market to mitigate the risk. The risk factors can potentially comprise of location of the property, demographic and economic factors as well as several physical property attributes such as property size, number of units, unit size, amenities, parking spaces etc.
Many organizations in the real estate investment management business struggle to integrate their internal data points and external/market data that could provide even richer sets of data for analysis. These organizations mostly rely on traditional reporting methods which only provide rear view or historical trends of their investments. Beyond providing historical trends, effective and comprehensive real estate analytics should reveal in real-time what is happening now and what will happen in the future. Though many analytical factors are shared across different asset classes (single family, multi-family, commercial etc.), the following paragraphs focus mostly on multi-family rental properties analytics.
The valuation of a property is one of the critical initial steps towards deciding whether an investment will yield positive or negative results. At minimum, in order to determine the potential value of a property, investors input multiple facets of real estate information into a financial model that compiles the data and generates various results. These results determine if investing in a particular property will yield positive results within the investment time horizon. When considering to invest in a particular property, the following preliminary data should be collected:
• Physical Property Details: This includes information like size, number of units, parking spaces etc.
• Financing Information: This would include the loan details (interest rate, amortization, loan terms etc.) that you would acquire to pay for the property.
• Purchase Details: The price you will have to pay to purchase the property plus any additional upgrades you would require to make the property rental ready.
• Income: This includes the monthly income that the property would generate
• Maintenance Expenses: Includes the cost of maintaining the property, managing the property, property taxes, insurance, and other costs related to general maintenance and upkeep of the property.
• Rent Roll: An itemized list of current residents by each unit and the amount of rent collected from each resident in a multi-family property.
• Tenant Profile: Includes things like credit history, credit score, employment history, family size, education, past rental history and more.
• Current and past operating statements: These statements include itemized list of income received from a rental property and the expenses incurred to maintain that property.
In addition to the specific property data mentioned above it is equally important to keep an eye on the current market environment including economic and demographic trends. In many cases the investment process starts with an analysis of the broader market to determine the feasibility of a location. Usually job growth markets along with favorable demographics contribute towards stronger and long-term tenant demand for apartments. For example a location with rising employment opportunities, income growth, and young adults (25-34 year-olds) living with their parents has a tendency for a pent-up demand for apartments. Below are few of the external data points that should be analyzed individually as well as in correlation with each other to determine the location viability of a property:
• Unemployment Rates/Income Statistics
• Consumer Debt as a Percentage of Disposable Income (Debt Barrier)
• Age Demographics
• Apartment Supply (multifamily permits)
• Vacancy Rates
• Rent Growth Statistics
• Urban Living Trends
The Following are few examples of graphs showing comparisons/correlations between different data points. Exhibit-3 below shows the correlation between gross income growth and vacancy rates. It is easy to see that the vacancy rate and the gross income move in opposite directions meaning that as the gross income increases the vacancy rate goes down (e.g. higher apartment demand). This type of analysis can also be performed at the local market level. For example in the specific MSA (metropolitan statistical area) where the subject property exists.
Exhibit-9 below shows the rent growth in different metro areas throughout the USA. Rents continue to increase the most in markets in California and Seattle. By having both macro and micro level market knowledge an investor can make an informed decision towards investing in a property.
Once a property or set of properties has been acquired it is important to monitor the performance of individual properties or portfolio of properties. There are two types of analysis that can be performed:
This type of analysis allows you to monitor your investment from different perspectives or dimensions. For example you may want to see the current market value of your investment(s) in different regions of the country or at a more granular level such as the MSA (metropolitan statistical area). You may also want to compare your average rent with the market rent over a certain time period. Another example of multi-dimensional analysis would be comparing the occupancy with the market occupancy in a local or broader market. The multi-dimensional analysis provides the ability to explore your data interactively.
Benchmarking is another example of multi-dimensional analysis where an organization would want to compare their investments with the industry performance. This is the type of analysis where quality external data plays an important role in the overall data management and analytics strategy. One such data source is NCREIF (National Council of Real Estate Investment Fiduciaries) which maintains an index that measures the performance of income producing real estate. Institutional investors still rely on this index to benchmark their investments. Investors usually want to compare their investment allocations against the index by property type and regions.
Data Mining/ Advanced Analytics
Data Mining is the process of finding hidden patterns in a set of historical data. This technique is used for both exploratory and predictive purposes. There are many statistical algorithms available which can be used for data mining. Few of the commonly used statistical algorithms are explained in the following paragraphs.Association Rule Mining finds associations and correlation relationships between several data points. Association rule shows attribute value conditions that frequently occur together in a given data set. For example price of a property could be associated with other attributes like year of sale, property type, location etc. This type of analysis can potentially uncover a hidden association in a large data set that may have not been visible through traditional reporting techniques. Special attention can be paid to those attributes that have a strong correlation with the property price.
Regression is a statistical algorithm that explains how a set of independent variables impact a dependent variable. In the case of a property, one of the dependent variables is the renewal probability of a lease, where as independent variables are the different attributes related to the property and external economic/demographic factors. The renewal probability ranges between 0% and 100%. The data collected is assumed to be thousands of lease renewals over a period of time from various MSAs (metropolitan statistical area). This type of analysis helps in providing a better understanding of issues that impact the probability of lease renewals and therefore can help in retaining tenants. The following are examples of a few independent variables that can be fed to the regression algorithm:
Number of Stories
Vacancy (an economic variable indicating vacancy rate in a sub market)
Employment (another economic indicator showing employment rate in a sub market)
Demographic factors related to tenants
Clustering is another data mining technique that groups similarly situated objects into a cluster. The goal is to identify sets of clusters with a high similarity within a cluster and low similarity between clusters.
This technique is very useful for segmentation purposes. For example in the case of property leases, tenants can be segmented into different clusters based on their shared characteristics. Segmentation can help in identifying different attributes of tenants which may result in long term or multiple lease renewals vs. the ones which terminate their leases pre-maturely. By analyzing the contributing attributes one can devise strategies to retain tenants or help in creating more effective and targeted marketing campaigns to attract the best renters.
Clustering can also expose trends that otherwise may not have been obvious through traditional reporting methods. For example, the lease renewal rate could be very high for a particular property leading one to conclude that operations are running efficiently. However, a clustering exercise could expose a cluster or group of highly profitable tenants who have been terminating their leases recently. This group of tenants may have been looking for certain amenities that are not available at the property at the moment. With this knowledge the property management can evaluate whether it is economically feasible to have those amenities built at the property in order to retain these high profit renters as well as attract new tenants with similar profiles.
With so many different data sources and volumes of data available it is challenging to collect, integrate, store, and manage all the data. But as long as real estate organizations are focused and set clear goals for their data management and analytics initiatives the opportunities are endless in data analysis and predictive analytics. Companies who are successful in combining different and varying data sources together to create multi-faceted analytics across these boundaries are positioning themselves to have the most unique competitive advantage in the marketplace.
For more information about implementing an effective real estate analytics program please contact firstname.lastname@example.org
About The Author
Shahid is an expert in Data Management and BI Architecture.
It’s 2016 and real estate firms are still running their business on spreadsheets!
by Ranjith Nair |
March 24, 2017 |
2016 is on track to be another great year for those of us in the real estate investment industry. An influx of international and domestic capital in recent years has breathed new life into the market. If the trend of financially viable deals continues, it presents a very real opportunity for fund companies and investment managers to significantly grow AUM – Assets Under Management.
Now’s the time to start thinking about how to manage it all.
About The Author
Ranjith Nair has over 19 years of experience in business intelligence and data warehousing solutions. Mr. Nair has worked on implementations across marketing agency, consumer packaged goods, real estate, software products, sales and professional services, and healthcare industries. Roles on these projects across various horizontals include user experience architect, data modelling, data architect, solutions developer and business analyst. Nair possesses a knowledge of business intelligence tools across Oracle (OBIEE) and Microsoft BI stack including: SQL server, analysis services, integration services and reporting services.
Choosing a Real Estate Investment Management Platform
by Alan Stein |
February 24, 2017 |
Once you’re ready to invest in a real estate investment management platform, it can be tempting to Google vendors, rush the process, and attempt to hurry the benefits. You may feel pressured to relieve your staff, improve operations, and impress investors. As a solution provider, we recommend that being slow and methodical is the best way to ensure you make the right decision.
There are many advantages of investing in an automation platform, and this piece will move along the decision-making process to provide an in-depth overview of the best practices for evaluating and selecting the real estate investment management platform that is right for your organization.
This approach can be tailored to evaluate many types of real estate software selections beyond investment management solutions.
About The Author
Director, Client Engagement
“Life is what happens while you are busy making other plans.” – John Lennon
What attracted you to Saxony? As for professional passion, I love the power of teams. People coming together to do things that can only be done through “team before self.” I love analytics—few things can be more enlightening than numbers that tell a story.
Expertise: I was co-founder and CEO of LiveLogic, an Inc 5000 Business Intelligence consulting firm in Dallas and Houston.
Education: BS and MBA University of Texas at Austin (Hook ‘em!)
Hobbies: I love hanging out with my wife Laurie, my kids Casey, Robert, and Eleanor, and our dogs Butch and Colt. I love traveling anywhere and everywhere. I love sports; I’m a huge Rangers, Cowboys, Mavericks, Stars fan and I bleed orange (Hook ‘em Horns). I’ve got a lifelong love-hate relationship with the sport of golf.
For most real estate investment firms, the ‘Model’ is the holy-grail for each acquisitions officer and asset manager. While there are several software platforms that have been successful in the modeling space, it always seems to come back to Microsoft Excel. Let’s face the facts – Excel is not going away anytime soon. Excel is the #1 system in the world for storing and modeling data, not to mention reporting. The question becomes: How do we take advantage of Excel while downplaying its weaknesses?
About The Author
Development Lead Consultant
Eric Hilton has over ten years of data warehousing development, design and implementation experience. He has spent his entire professional career in the business intelligence and data warehousing space. Mr. Hilton has held a variety of roles on projects through his career: data architect, ETL architect, report developer and ETL developer.
by Jeff Wilson |
June 2, 2015 |
The Inevitable Investment
Historically, companies have added staff as AUM has increased. In fact, many organizations have seen parallel growth in head count and AUM. While other industries have leveraged technology to automate routine tasks, the real estate industry has lagged behind and pushed Excel to feats that would impress Bill Gates. Fortunately, there is a better way to manage the business.
The “Life of the Asset” Challenges
During the early stages, acquisitions officers often work independently and use a variety of spreadsheets and software to track the real estate deal pipeline. This information is rarely integrated with the rest of the firm outside of the investment committee package. Key information, which is important to the rest of the organization, ends up siloed in Excel or a deal pipeline system.
Underwriting is done via Excel or another financial modeling tool and stored on a file share. When models are updated, versions are often not saved. When relying on Excel, companies often have the problem of properly evaluating performance against the original underwriting models.
The financing pipeline is tracked in Excel and rarely shared outside of the acquisitions team or capital markets group working the deal. During the closing process, documents are stored in a file share, content management tool, or even a filing cabinet. As a result of this process, valuable information needed by the entire firm becomes locked away on paper, hard drives or email.
While these problems are significant, they become more troublesome when the deal is finally closed. At that point, portfolio and asset management team members need the information that the acquisitions, underwriting, financing, and closing teams have been keeping in their respective silos, and the effort to organize and transition what’s needed post-close becomes a Herculean effort or chaotic at best.
The problem only gets worse from here for most real estate investment managers. Asset managers require updated accounting, financing information, and rent rolls to perform rolling forecasts/projections. Because this information is stored and maintained in disparate off-line systems, Excel becomes the only tool available for reporting.
Because of these disjointed processes, monthly and quarterly reporting becomes an all-hands-on-deck manual effort. The amount of human involvement provides a myriad of opportunities for errors that could result in reputational damage, not to mention burning out key employees. Continual one-off reporting requests add to the already enormous amount of manual workload resulting in a waste of key employees’ time and the firm’s money.
In addition to internal needs, investors are demanding quicker turnaround in reporting cycles, both in standard reporting and ad-hoc needs. Investor reporting demands are driving real estate investment firms to provide real or near-time access to fund and portfolio performance information. These reports are often generated in Excel and either posted to a shared drive or an investor portal. More savvy investment firms have automated some of the reporting to the investor but there is significant legwork behind the scenes to provide this transparency.
Historically, firms have increased head count to manage the workload with an army of IT staff to develop custom solutions or extra employees to handle the manual processes. Throwing people at the problem provides no economies of scale.
Excel Can’t Keep Up
Excel can be a useful tool for modeling, ad-hoc reporting, and what-if scenarios, but it is not a database, nor is it easily accessible to a large audience. To keep pace with the market without increasing unsustainable head count in downturns, firms should reduce reliance on Excel and manual processes and look to standardize.
Let’s take a look at how automation can work for the real estate investment industry:
Automation in Action
It’s a very different day in the life of the asset when asset management firms are fully automated from the point of acquisition to investor-ready reports.
• The deal pipeline is integrated with the rest of the platform.
• Final underwriting assumptions and models are captured in central database for asset management to perform look back/performance reporting.
• The closing team has automated checklists and workflows that track the status of the transaction.
• Fund-level returns and what-if scenarios are managed with modeling, financing, and reporting information.
• A complete view of the portfolio provides advanced reporting and tracking of key metrics.
• Financing information is integrated with accounting and other key asset management information to facilitate reporting needs.
• Assets are seamlessly managed with tenant/lease data, budgets, accounting, debt and property information, valuations process and reporting, and development and construction tracking.
Investing in Automation Software
The technology behind a real estate investment management software program is complex, but using it should not be. When reviewing your options, check first for usability, as user adoption is key to success. Also, look for accessibility and flexibility as the software should allow users access from anywhere and be configurable to meet their needs. Automation should manage the complete life of the asset, starting with acquisitions and continuing through closing and day-to-day asset and portfolio management. Reporting should be simple and streamlined and provide transparency for investors.
Your team may have some initial reluctance in giving up their Excel spreadsheets. Old habits die hard, but the long-term benefits will pay off.
What’s more is, all data and records are organized in one location—clean and simple. Since everything’s together, reporting is programmed into the system one time and ready for investors whenever it’s needed. No more chasing around data at the last minute. And for that, your peers, employees, and investors will thank you.
About The Author
Founder & CEO
“I am the vine; you are the branches. If you abide in me and I in you, you will bear much fruit; apart from me you can do nothing.” – John 15:5
Hobbies: Running, Serving PCPC as an Elder, spending time with family and friends
School: Ole Miss
Saxony Partners: It has become an organization that is larger than the sum of it’s parts. We are an amazing team of professionals who enjoy what we do and who we do it with. Jeff founded Saxony Partners in 2011 after recognizing a need to provide a comprehensive “Life of the Asset” technology solution to the Real Estate Investment industry. Prior to starting Saxony, Jeff served as the Technology Chief Operating Officer for the Archon Group, the Real Estate division of Goldman Sachs. Previously Mr. Wilson worked for Arthur Andersen Business Consulting in their Real Estate Division. Jeff is an avid runner and is an elder at Park Cities Presbyterian Church in Dallas.