According to Kent Larson, urban planning for the future is all about optimization and efficiency. Using data, we can create models that can help us understand why people choose to live where they do, what urban spaces can and should be used for, and even where people choose to spend their money. All of which can lead to smarter, safer, and more sustainable cities. 

Transcript

Today, urban planners are embracing the emergence of open data to better understand current conditions and trends. Transportation planners in particular are building powerful simulation models to predict conditions such as traffic flow and congestion. But most innovation has been focused on optimization and efficiency.
So I will briefly take you through what led to the CityScope platform that we’re currently deploying in many cities around the world.

We started with a data unit, a Lego module, and this was used to build studies of cities with color-coded Lego bricks, where for example, blue was water, green was parks, yellow was retail, black was housing, et cetera.
So this is the CityScope platform, a data-driven, evidence-based platform that allows for the modelling, of various urban planning and system integration scenarios where we can, for example, study the mobility modes, we can look at different land uses in a district, we can model the solar radiation patterns or the wind flow… These are fairly easy simulations because there are commercial tools out there that allow for this.

More interesting, to me, is the visualization where the money’s flowing. We can look at geo-located tweets – so where are young people active? So this is a geo-located twitter feed that can show, for example, the Media Lab being very bright, the computer science lab being very bright, the area with all the restaurants on Third Street being very bright, and other areas of the district not as bright indicating activity.

Now what we’re doing is developing an interactive CityScope that has these elements; optically tagged modules that can be mapped to any database that we care to – land use, construction costs, profiles of occupants, et cetera. We then project heat maps for various visualizations on the table. We have the radar plot updated in real time, as people make changes to the table; key indicators, showing the innovation, potential; the flows of people and vehicles; and a three-dimensional view showing the resulting zoning envelope.

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If you ask Dr Andrea Chegut, one of the most exciting aspects of applying data science and machine learning in the built environment is the possibilities it has to offer. For example, being able to create a ‘digital twin’, namely taking a physical asset like a building and turning it into a digital replica, will allow us to push the boundaries of what’s achievable in everything from architecture and construction to finance and demolition.

Transcript

There are many ways in which data science and machine learning can be applied in the built environment.

These can impact the real estate industry in ways that you should consider. Let’s look at how data science and machine learning has been used so far and how it has impacted individuals, companies, and the built environment. One area that is often not looked to, but is actually really a driving force is the construction sector.

More than 50 years ago, astrophysicist and engineers envisioned a way to be able to work on technologies that were physical assets and turn them into digital replicas, so that they could enable and envision a stimulated and fabricated future of a physical thing without having to change it. The built environment has caught on to this technology, which is called a digital twin. The idea where I can take a physical thing and create an exact digital living replica of it.

This technology is fundamentally driven on the idea that we get the basic data, the basic nuts and bolts of a physical object, like a building or transportation infrastructure, and then we take that data and we enable it to be transformative or time bearing with sensors. When we take this pairing of basic static data and pair it with living time sensitive and dynamic data, we create an environment where we can start to envision a dynamic digital twin of a living, breathing, physical thing and watch it how it changes over time.

Now the power of this technology for the built environment is for us to be able to understand what we could do in a physical asset without having to fully build it, or to enable changes to something that is physically, already built, but enabling different futures or programming possibilities. The power of this technology will impact construction, architecture, finance, demolition, you name it.

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In the context of real estate, leveraging can be ‘high risk, high return’. But why does Dr. Jefferson Duarte call it a double-edged sword? Although borrowing money to invest in property (or leveraging) can lead to much greater returns, the flip side is that when things don’t go according to plan, it can also lead to much heavier losses. 

Transcript

So what happens when you have to borrow money to acquire property? How does it affect your risk and return?

In finance, leverage is a term used to describe the levering effect of debt in your capital structure. Suppose you invest $100 of your own money and expect to receive $10 as a return. This means that your return is 10 percent in expectation.

Now, suppose we invest $40 of your own money and borrow the other $60. The underlying investment is expect to earn the same $10 return and you pay 5 percent interest on the $6 that you borrow. The value of your return is therefore $10 minus the interest expense of 5 percent, multiplied by $60, which is equal to $3. That is your return is now $7.

You can see the percentage of return increases from 10 percent when you were only investing your own money to 17 and a half percent when you include the $60 of borrowed money in that investment. This levering effect increases as you borrow more money, as you can see in this table.

If borrowing money to invest yields much greater expect returns, then why not always borrow as much money as possible when making an investment? The answer is that leverage is a double-edged sword. While it does enable much higher returns, it can also result in more significant losses.

Let’s go back to the example of investing $100 for your own money. Only this time, let’s assume that you lose $10 instead. The underlined investment loss in percentage terms is therefore 10 percent. However, if you use $40 of your own money and borrow the other $60, what is your loss in percentage terms? 

So as you can see, the percentage of return decreases from minus 10 percent when you’re only investing our own money, to minus 32 and a half percent when you include the $60 of borrowed money in that investment.

As with the positive effect of leveraging, the effect is amplified on the negative side as well. When it comes to risk, the effect of leverage results in much higher volatility. The upward swings are much larger, but so are the downward swings.

When you choose to borrow money for your investment, you are creating a split between debt and equity. The underlying value of the asset equals the debt or loan value, plus the value of your money or equity.

Leveraging increases both the potential returns and the potential loss of an investment. This increased volatility leads to increase risk and higher expected returns.

There are a number of considerations to take into account when deciding whether or not to incorporate leverage into an investment. For example, is the interest expense tax deductible?

The post How to Leverage Debt in Real Estate appeared first on GetSmarter Blog.

As Professor David Geltner explains, in terms of real estate, no risk often means no (or, rather, much more limited) returns. So how do you decide whether an investment is worth taking on more risk? Well, it’s all about market equilibrium and expected returns.

Transcript

Imagine for a moment that you have the opportunity to invest in either one of two assets: Asset Charlie and Asset Delta. These two assets offer the same expected return. That is, over time, their returns will tend to average out the same. However, Asset Charlie is less risky than Asset Delta, meaning that Asset Delta’s returns will tend to bounce around or rise and fall more. Which one of the two assets would you invest in?

Well, if you’re like most investors, you would choose Asset Charlie, the less risky of the two since the expected return is the same. But what would happen if Asset Delta offered a higher return compared to Asset Charlie. Now, perhaps your decision is not so obvious.

Let’s take a look at how this works in the market for assets, such as stocks, bonds, and real estate. This graph shows the relationship between risk and expected return in the asset market. The line on the graph is called the security market line or SML for short. It was originally used in the stock market, but applies to all capital assets, including real estate.

Investment risk is measured on the horizontal axis and the expected return is measured on the vertical axis. The risk-free rate is the return you can get by investing in an asset that has no investment risk.

Refer back to the example of Asset Charlie and Asset Delta, investors would rather invest in Asset Charlie. Investors would try to sell Asset Delta and bid to buy Asset Charlie. Demand for Asset Charlie increases and demand for Asset Delta decreases.

This will drive up the price of Asset Charlie and drive down the price of Asset Delta. The future cash flows the assets can generate are not affected by all this bidding by investors to buy and sell. Returns are essentially future cash flows as a fraction of present price.

So, as the price of Asset Charlie is bid up, its expected return is effectively bid down. And vice versa for Asset Delta. This will result in Asset Delta, the riskier of the two assets, offering a higher expected return to investors than Asset Charlie. At a certain difference between the expected returns of the two assets, investors, on average or overall, will be indifferent between them, and the market will be in equilibrium with no pressure on either assets, price, or expected return.

The amount of difference between the two assets’ expected returns, in order to achieve this equilibrium, depends on the amount of difference in the investment risk in the two assets. And it depends on how risk averse the market is, and on investors preferences for lower risk assets. This is reflected in the slope of the line in the graph. 

Capital markets must compensate investors by pricing assets to provide higher expected returns – that is, higher returns on average over the long run on riskier investments.

The security market line can be represented graphically as we’ve shown it here, defining risk on a single dimension. Remember that the risk-free rate is the rate you are guaranteed to get on an investment that has no risk. This rate is also referred to as the time value of money, and it only compensates investors for the fact that they were allowing others to use their money over time.

The risk premium is the additional compensation in the form of higher expected return, average return over time, to investors for their willingness to take on more risk. It is the difference between the total return and the risk-free rate. The risk premium must be proportional to the amount of risk the asset is exposed to.

Looking at the graph again, you will see that, for every additional unit of expected return, the investor must take on the same additional unit of risk.

With this in mind, let’s step back a minute and view the return components relationship more broadly. It is important to note that the graph illustrates an ex-ante relationship.

In other words, the expectations the market has beforehand of what will happen with the various investments. Ex-ante investors can only increase their expected return by taking on more risk. However, in reality, the ex-post return will often differ from the ex-ante expectations. If an asset always returned exactly its prior expectation, there would be no investment risk.

This ex-post difference between the realized return and the risk-free rate is called the excess return.

In this video, you saw that riskier assets must provide investors with higher returns, otherwise investors will not be interested in buying these assets. The opposite is, however, also true. You cannot expect to receive higher returns on an investment on average, without taking on more risk.

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Ever wondered how property prices are determined? According to Dr Gabriel Ahlfeldt, it’s all about supply and demand. While there are many other factors that can influence how these prices are set, beyond what people are willing to buy for and sell at, a good place to start is by determining the market equilibrium.

Transcript

Have you ever browsed through property advertisements and wondered how the property prices were determined? We can use supply and demand theory to help us gain an understanding of some of the factors that determine how these prices are set. 

The demand curve represents a range of theoretical prices at which buyers are willing to buy certain quantities. The supply curve presents a range of prices at which sellers are willing to sell certain quantities. But how do we determine what the actual price and quantity offered in the market are? This is done by determining the market equilibrium, which is where the quantities demanded and supplied are the same. 

The demand and supply curves indicate the prices at which products are bought and sold respectively. If you look at the supply and demand curve jointly, we can determine the market equilibrium, which is the point at which supply and demand curves intersect. At this point, buyers want to buy exactly the same amount that suppliers want to sell. The market equilibrium implies that demand and supply are equal. There is one quantity and one price where this condition is met.

How does the market converge to an equilibrium? If prices are lower than equilibrium prices, demand will exceed supply, as individuals are willing to buy more than what producers supply. This will lead to a shortage and consumers will try to outbid one another until the price goes back to the equilibrium price. The same happens when the prices are above equilibrium. There will be an over supply as supplies are willing to produce more than what consumers demand. This leads to a fall in prices, which reduces production until demand and supply are equalised. 

Therefore, in the long run, market prices will tend towards equilibrium. Now suppose that there is an increase in demand in the market. Typically, demand shifters include population growth, income growth, or a change in preferences. In the housing market, demands increase as good results because of a change in housing structure, such as an increase in single households or single-parent households with corresponding higher per capita housing space consumption. This will mean that at any given price, we have a larger demand for housing and a shift in the demand curve to the right. In this case, with a positively sloaped supply curve, the shift and the demand curve will result in a new equilibrium price and quantity, both of which are higher than before.

The same rationale applies to the supply curve. Typical supply shifters include the change in construction costs or wages, or a change in policies or planning such as new safety requirements or design standards. The production of housing units therefore becomes more expensive, so that at any given price a lower quantity is provided. Therefore, a new equilibrium, where we have higher prices and low quantities, emerges. 

One characteristic of demand is that it tends to increase over time. This is because the population grows and income generally tends to increase over time. Given that the supply curve is not normally fully elastic, we observe over time an increase in the price of housing. The increase in the price is now determined by the slope of the supply curve. Therefore, if we are in a situation where there is more restrictive planning or we are in a location that is subject to create a land scarcity, in other words, the supply curve is steep, then we will, in the long run, observe higher prices. Both the price level and price trends being the increase and decreases in prices over time are determined in the long run by the interplay of demand and supply. In the long run, the slope of the supply curve is critical in determining increases in housing prices.

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According to Andrew Baum, for anyone who is considering buying or selling a house, looking into an investment property, or managing a large property portfolio, it’s important to know the market value of such a significant asset. A simple way to accomplish this is by using one of three different valuation approaches: direct capital comparison, the income approach, or the cost approach.

Transcript

So why is valuation important? And, in this context, we were talking about valuation as a way of assessing the most likely selling price of something.

 What it’s really about keeping the score, and for a variety of purposes, people need to keep the score.

Imagine, for example, that you are a large property owner, how do you know what your balance sheet looks like? You need to know what the most likely selling price of your property assets is and are in order to assess your asset value. Keeping the score in a balance sheet.

 If you’re an investment manager, how well is your portfolio performing? And in keeping that particular score, you need to know how the capital value of your property assets has improved or maybe fallen over, let’s say a year, in order to assess your performance, your total return on those assets.

There’s also a strong need for, for property valuations, if you are a bank lending money secured against those assets. Property investors use a mixture of their own equity and, and lenders debt in order to buy any assets. And that’s true for all of us buying houses and bank-lending or mortgages. The bank will want to know what our property is worth, in the event that they have to take it back and sell it to recover their loan.

 When we think about assessing the most likely selling price of something or the market value, we’re likely to use one of three different valuation approaches.

These are direct capital comparison, the income approach, and the cost approach.

Direct capital comparison is most likely to be used in a homogeneous sector like residential property, where we have lots of similar houses or apartments, where we can use the selling price of one apartment as an indicator of the most likely selling price of a very similar apartment nearby.

The income approach is a way of building a discounted cashflow model for the most likely cashflow, that an investment property will throw off. So if we’re buying a residential property in order to receive a rental flow from it, what will that rental flow be? How can we discount that rental flow, and what will the resulting present value of that cashflow be?

And then finally, in a rare number of cases, it may be that we can’t use direct capital comparison because the asset is much too individual, or rare or unique, and we can’t use the income approach because it produces no income.

 So let’s, let’s take as an example a university building in Oxford. It’s very unlikely that there are any direct comparisons of recent sales. And it’s probably very unlikely that this building is achieving a market rental income that we can discount. The only thing we’re left with is working out what it would cost to build that building. How much would the land cost, how much would it cost to build the asset? And then we would make an adjustment for the depreciation of the building, to get the best possible estimate we can of something approaching a value, a market value.

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Understanding the term ‘big data’ gives context to how it can be applied in real estate analysis. Gartner’s explanation is considered the go-to definition: “Big data is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.”¹

Essentially, the term refers to the processing of large amounts of data, be it historical or real-time. Algorithms are applied to unveil trends in user behavior, increase speed to market, target customers, and ensure they’re satisfied. The data sets can be structured or unstructured, with information sourced from a vast and growing range of inputs. Processing, analyzing, and identifying meaningful insights from such volumes of data is too great a task to expect from a single analyst or more traditional data processing software, which is why specifically designed software is utilized to do the analytical heavy lifting for organizations. The analysis can be done in-house or by third-party specialists, who can also help companies manage the sheer scale and noise of data.²

The five Vs of big data

The three Vs of big data, as conceptualized by Gartner, have now become five:

1. Volume: Big data entails processing high volumes of data, from social media data and clicks on a web page to data collected via mobile apps or sensors. Volumes range from terabytes to petabytes.³
2. Velocity: To yield the most relevant and recent insights, and thus inform the best decisions, data needs to be gathered quickly – as close to real time as possible. Some see the speed at which data comes in as more important than the volume.⁴
3. Variety: This refers to the wide array of available data types. Traditional data types are more structured, whereas big data typically comes in unstructured sets. Analysts often need to pre-process unstructured and semi-structured data types, including text, audio, email, and video, before they can extract value from them.⁵
4. Veracity: Increasingly important in this age of information overload is the quality and trustworthiness of data. Managing large volumes of data can be more cumbersome than useful if the data isn’t complete and accurate.⁶
5. Value: Finally, organizations need to monetize their data by transforming it into a business advantage. Gaining useful information about customers to serve their needs better is one key way to create value.⁷

The power of data

Big data can prove effective in getting your message in front of your target audience and measuring its efficacy in a world already cluttered with advertisements and content.⁸ It helps reveal consumer trends, patterns, correlations, and customer preferences.⁹

The key to using big data efficiently isn’t in how much a business has, but how it utilizes it. Organizations can take data from any source, and in any format, and analyze it for solutions to:

• Make better decisions: Big data analytics can give business decision-makers the insights they need to help their businesses stay competitive and grow. However, doing so requires more than just collecting and analyzing information; leaders need to work hard to drive culture change to grow truly data-driven organizations.¹⁰
• Cut costs: The smart use of big data can help businesses cut costs in several ways. These include reducing marketing spend through targeted advertising, optimizing supply chains, identifying potentially fraudulent customer activity, and resolving production problems faster.¹¹
• Increase productivity: Analyzing data points relating to performance metrics, employee output vs. hours worked, the effectiveness of business tools, and client-specific profitability can help organizations better focus their efforts and resources.¹²
• Manage online reputation: A survey of more than 2,000 executives found that, on average, 63 percent of company market value could be attributed to reputation. In this context, monitoring all information about a business is crucial, including social posts, surveys, and customer reviews. Analysis of this information should constantly be used to inform and update business listings, marketing strategy, and advertising.¹³
• Improve customer service: By analyzing customer interactions across data points, including searches, purchases, feedback, and reviews, and identifying behavior patterns in these, organizations can improve their service in a host of ways. They can use this information to build customer profiles and personalize their services accordingly, improve their products and operational activities, anticipate market needs, and provide more responsive support.¹⁴
• Increase revenue: Businesses can use the insights about customer preferences and order histories to drive targeted sales and develop more appealing products. Data analysis can aid in price optimization and better-tailored business strategies, which can lead to improved revenue.¹⁵

The advantages of big data in real estate

Before big data, many of the decisions made in real estate were mainly based on experience and limited analysis of trends. Now, property professionals use real-time, detailed information from multiple sources to make more informed decisions.¹⁶

Real estate service providers who focus on delivering bespoke, customer-centric property solutions can increase customer satisfaction. In addition, data insights can be used to help meet the needs of buyers and sellers. In this way, service providers can position themselves as the real estate partners of choice for prospective customers.

Searching for a property today – to buy or rent – is an online exercise conducted through apps, websites, and online forums. According to a 2021 study by the U.S. National Association of Realtors, 97 percent of home buyers used the internet to search for homes, while 51 percent found the home they purchased online. The most common actions taken through such internet searches were:¹⁷

• Virtual walk-throughs
• Viewing the exterior of a home or neighborhood
• Finding an appropriate estate agent
• Pre-qualifying for a mortgage
• Requesting more information

Realtors, investors, home buyers, and financial institutions now have access to data that’s a click away. This empowers them to make more savvy decisions, with data analysis facilitating more accurate predictions about risk and market trends.¹⁸ Benefits include:

• Risk mitigation. Predictive analytics helps assess commercial viability and thus reduce risk when it comes to real estate investments, with realtors and buyers now having access to accurate metrics drawn from reliable real estate data.¹⁹
• Simple (and fast) evaluations. Realtors use property evaluations to set the price of their properties, and home buyers and investors use these evaluations to put forward offers. Financial institutions rely on them to calculate loans and minimize losses. Through real estate data analysis and statistical modeling, appraisals can be made based on years of market data. This is increasingly happening automatically, alongside services like Opendoor, which automatically bids on houses.²⁰
• Understanding customers’ needs better. Predictive analytics provided by big data helps real estate agents better understand what their customers want and helps them respond with personalized offers based on the data.²¹
• Improved marketing strategies. Realtors can identify potential customers across a range of data points, including age, gender, preferences, interests, and region, as well as whether or not they have children or pets. This more granular approach enables more targeted and efficient marketing.²²
• Market trend forecasting. Tracking additional data points such as employment trends and income levels can grant insights into potential future events such as foreclosures, spikes in prices, or the needs of specific groups.²³
• New insurance services. Insurance companies that cater to home insurance analyze data from various sources to develop and personalize insurance offerings for customers and geographic regions.²⁴

The future of big data in real estate analysis

Ever since Google and YouTube added ‘how-to’ videos for almost every subject, people have discovered how to execute any number of tasks previously reserved for professionals. This includes handling real estate purchases. In the U.S., organizations such as CoreLogic, Smartzip, and InfoSparks offer interactive data visualization, providing important data to use for accurate decision-making.

Data analytics technology is becoming increasingly user-friendly, making the tools more accessible to people from all walks of life. The latest technology is not only optimizing the real estate industry but also improving the way we live in our homes. Examples include:

• Smarter building management: Internet of Things (IoT) sensors can help building managers monitor things like elevators, heating and air-conditioning systems, and ventilation, sending alerts if there are malfunctions. When analyzed over time, this data can enable predictive maintenance, improved tenant and resident experiences, and reduced costs.²⁵
• Transparent data democratization: The growing volume of data created through property technology platforms and digital tools is driving transparency in real estate markets. This will ultimately facilitate greater investment.²⁶

What does big data mean for the real estate industry of tomorrow? Advances in technology will continue to reshape the industry and drive more personalized customer experiences. These changes are likely to alter responsibilities, required skills, and risks in the real estate sector, and will impact margins. However, the long-term outlook for the real estate industry in terms of big data and analysis is a positive one. It’s up to industry professionals to learn how to use the analytical tools necessary to make data-driven decisions, preparing themselves for the future of real estate today.

Harness analytical and statistical techniques with an online short course

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Financial technology – or fintech – is significantly impacting real estate financing for houses and mortgage lenders. In the USA, the market share of online mortgage lenders has quadrupled over the past six years. In 2016, Quicken Loans launched their fully online lending service that generated an increase in their total loan amounts of 22 percent, and LoanDepot.com’s digital lending platform saw their numbers grow by 40 percent, where Wells Fargo and Bank of America had only 5 percent and 1.72 percent growth respectively.⁵ In fact, in late 2017 and early 2018, Quicken Loans overtook banking triumvirate Wells Fargo, Bank of America and Chase Bank, as the top originator of residential mortgages.⁶ By 2020, the largest mortgage lenders, in order, were: Quicken Loans, United Shore Financial, Freedom Mortgage, Wells Fargo, LoanDepot.com, JPMorgan Chase, Caliber Home Loans, Fairway Independent Mortgage, Bank of America and U.S. Bank. Quicken Loans originated 1.1 million loans, nearly double that of United Shore Financial, with 560,798 loans.⁷

Fintech is growing quickly, with a sharp focus on the banking and financial industry. While we’re exploring fintech and mortgages here, it’s important to keep track of the upcoming trends in fintech innovation across sectors if you want a full overview of how it’s changing the way people approach money. For those interested in leveraging fintech, you can also learn how to fund your own fintech idea on the University of Cape Town (UCT) Fintech: Disruption in Finance online short course.

Why fintech is overtaking traditional mortgage lenders

A technology-based company that tries to improve financial services is considered a ‘fintech’ and typically promises to provide faster, more accurate, safer, and more affordable financial services options than traditional mortgage lenders. It also offers consumers ease of access (such as mobility), increased transparency, and the ability to consolidate and aggregate across many different platforms.

Fintech mortgages are faster than working with traditional mortgage lenders

Compared to traditional mortgage lenders, fintech lenders can reduce the time it takes to close home purchases by around nine days. With regard to refinancing, the difference is an even more enticing 14 days shaved off from the closing process, on average.⁸

Fintech is able to offer more affordable interest rates than traditional mortgage lenders

When looking at personal loans, fintech firms offer lower interest rates than traditional nonbank loans, and similar interest rates to traditional banks. For mortgage loans, interest rates vary by less than a percent between fintech and traditional banks. Overhead costs, as well as operational costs, might be the reason that some fintech interest rates are lower than that of banks.⁹

While interest rates tend to fluctuate, purchasing a home is an investment, and a large one at that. The London School of Economics and Political Science (LSE) Real Estate Economics and Finance online certificate course explores how to evaluate property investment options to make informed decisions.

Fintech mortgages offer more flexible requirements than traditional mortgages

Fintech mortgages come with many benefits. Whether it’s that the company is available 24/7, or that you can apply from your home computer, when you compare mortgage loan provider Rocket Mortgage (RM), for example, with a local bank, the benefits of a fintech company are apparent: ¹⁰

• Both require two or more years of verifiable income history
• Local banks may require good or better credit scores, while RM allows for fair or above-credit scores with a down payment
• Local banks ask for a debt-to-income ratio that’s 40 percent or less, while RM allows 50 percent or less
• Local banks require the home to be a primary residence where the bank serves, while RM allows for single family, multifamily, or other nontraditional mortgage type
• Local banks require borrowers to live somewhat locally, whereas with RM the borrower can live almost anywhere
• Local banks typically require down payments of more than 10 percent, where RM may accept down payments as low as 0 percent

The flexibility of fintech mortgages allows those seeking a mortgage to place some of those funds into investments rather than a down payment. For those already comfortable with fintech and online banking, the next logical step is wealthtech, which helps manage wealth through artificial intelligence and machine learning. Have a look at how wealthtech offers another opportunity to manage finances to learn more.

Fintech mortgage startups are disrupting the industry

More than 66 percent of mortgages in the USA are by fintech and nonbank lenders, with Rocket Mortgage – previously known as Quicken Loans¹¹ – leading the way. This is opening the door for fintech start-ups to disrupt the industry, by making it easier for prospective homeowners to finance their new homes:¹²

• Zillow Group, founded in 2006, has grown to a company with more than $2.7 billion in revenue
• Fiserv, established in 1984, provides technology to the financial industry and has done so before fintech was fintech, providing solutions allowing for loans to be created more quickly
• SS&C Technologies Holdings, founded in 1986, provides end-to-end software, and reported $4.63 billion in revenue in 2019
• Fair Isaac Corporation, otherwise known as FICO, is related to credit ratings and helps lenders determine how individuals compare to other borrowers. FICO reported $1.16 billion in revenue in 2019

While fintech start-ups are disrupting the mortgage industry, fintech is also changing other financial services. The FinTech online short course from Harvard’s Office of the Vice Provost for Advances in Learning (VPAL) explores the current impacts of fintech, and how future financial services will be impacted.

Fintech is being embraced by traditional financiers for financing real estate

Where most banks are still stuck in traditional credit checks, legal work, paperwork, and three-month-long mortgage financing processes, there are financial institutions embracing fintech to finance real estate:

• The Bank of Russia is working on Masterchain, a blockchain platform launched in 2017,¹³ that will be used for digital mortgages¹⁴
• Previously, a Russian-based subsidiary of Raiffeisen Bank International used blockchain for mortgages¹⁵

As other countries adopt fintech and merge it with their traditional mortgage providers, regtech, the regulations of fintech are being explored. Explore the development of regtech to learn more.

Digital lending platforms and the internet are fast becoming an important channels for mortgage providers, with rapid technology innovation leading to greater competition from fintech mortgage start-ups. For financial institutions hoping to compete, a seamless, personalized loan process provided through technology will be to their advantage. A great way to do this is for banks to partner with fintech start-ups for their non-core activities in order to grow their competitive edge and improve their customer proposition, while still focusing on their core activities. This type of partnership is beneficial for fintech start-ups that need access to a larger customer base and a steady financial base.¹⁶

Understand the finance behind real estate

THE LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE (LSE)
Real Estate Economics and Finance online certificate course

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The construction sector is growing at a rapid rate, resulting in managers often neglecting industry standards in favour of creating more at speed. But as the construction sector in sub-Saharan Africa continues to develop its infrastructure and show great investment potential, construction managers will need to prioritise quality over quantity if they want to reap the rewards.

Transcript

The construction industry’s commitment to quality, at the moment, in South Africa, in summary, I would say, is very poor. Quality is currently not a priority. Many people are just hoping that, in all the rushing, they hope that they get a building of good quality, but it’s not the first thing that everyone is thinking about when they walk onto a construction site.

Analyse quantity and cost versus quality

Because of the triangle of time, cost, and quality, whenever you push one of the two, then one of the three is going to suffer, and at the moment, with the pressures of the industry and of the economy, quality is suffering because of the time that is being pushed, of the cost that is being pushed, trying to save money, and, at the end of the day, the quality is then neglected.

Understand the implications of poor quality management practices

Unfortunately, the clients are the ones that are mostly affected by poor quality standards because they’re the ones paying, and they’re the ones receiving the product at the end of the day. So, they’re the ones that lose the money. They’re the ones who do not have time to recover any of the time lost during the project, and they’re stuck with the product that they didn’t like in the first place. Even if someone may want to sue the contractor, they may get some of their money back, but to be honest, you’ve lost the time, and most clients are developers, and they have tenants that are waiting to move into these buildings, and, at the end of the day, they are the ones that really do lose out.

Discover the consequences for neglecting quality standards

If you don’t manage your work properly, then your reputation goes down the drain. I always say that you’re only as good as your last project. So, if you mess up on one project then, unfortunately that’s what your name is going to be pinned onto. And the other thing is reworks. And the cost of reworks is very high at the moment, and I personally believe that it’s because contractors don’t have quality systems in place. So, because of that, you now have reworks, you now have to divert some resources that you had assigned to other projects to go back to redo work that you’re not going to get paid for.

How stakeholders can influence industry standards

The stakeholders in the South African industry, at the moment, I think they can contribute into ensuring that quality becomes a priority, if I can put it across that way. If, for example, standards are just raised that this is now a priority, this is now a regulation, this is now as per tender, then I think that that would also help to just raise the bar of quality and making sure that people are trained, if the stakeholders in the industry put certain standards in place to say that, “I need to see an artisan card for each and every person who’s working on-site” like they used to do before, then we would see a dramatic increase and a dramatic improvement in the quality of work that is produced.

The post Why We Need to Prioritise Quality Over Quantity in South African Construction Management appeared first on GetSmarter Blog.

Property remains a potentially lucrative industry, however many first-time owners underestimate how much it costs to maintain a property and overestimate the potential income that property could bring. This is why it’s essential for new buyers to have knowledge of market trends, property laws, and nuances such as lease agreements, and rates and taxes to ensure they make wise investment decisions and are prepared for the possible risks involved in buying property.

Transcript

There are a couple of mistakes, but one of the key ones is they underestimate the operating costs of running a property. They often underestimate the cost of utilities and what that does within the context of property ownership. The other thing they do is they overestimate the income projections associated with property and they have a too optimistic view of what the underlying property is worth.

And then first-time owners are often romantic about property. So they equate the value to the bricks and mortar of a property, the physical structure. Whereas the value is what a willing buyer would be willing to transact on.

Understand the legal hurdles as a first-time buyer

Property is quite unique in that most relationships that are tied to a property are transferred to the new owner. So one has to be very careful in understanding the status of the property in terms of existing leases, in terms of notarial deeds that are tied to the property. The important point that I really wanted to pick up, is just how sensitive the law is in terms of illegal occupational property. For instance, if anyone occupies a property, you’ve got 24 hours to actually evict or remove them before they get rights to be on that property.

Examine the risks and liabilities as a property owner

As a property owner, you need to understand that you are liable for certain expenses on the property: rates, taxes… And as a new property owner, you need to understand what the historic position is with regards to those, because you may be liable for those. What’s also happening in the municipal space is there’s an increasing focus on compliance, with respect to occupancy. So we’re talking about fire, water, and electrical utilities and reticulation, and on older properties, that could be a substantial cost.

The post The Common Mistakes First-Time Property Owners Make appeared first on GetSmarter Blog.