Housing and construction fees have skyrocketed over the past few years. This increase goes back to multiple factors: economic unrest, raw materials disruption, and labor shortage, to name a few. What does web data have to say about all this?

In this week’s “Alternative Data Trends” issue, we’ll talk about commercial real estate, unveiling the industry’s ESG and SDG conformity and the effects of COVID-19 on the supply chain and labor.

Commercial real estate volume of mentions

While analyzing web data dealing with commercial real estate, we detected an evident increase in the industry’s volume of mentions. This trend spiked in April 2020 and was initially hindered by the COVID pandemic, which resulted in a drop in sentiment polarity. Still, it witnessed a rapid recovery leveraging digitalization and e-solutions (Figure 1).

Case study: Unibail-Rodmaco-Westfield

To further understand the commercial real estate industry, we studied Unibail-Rodamco-Westfield and its competitors. Unibail-Rodmaco, a French commercial real estate company, acquired Westfield, a U.S. company, in December 2017. This acquisition accentuated its market share and grew its web voice share compared to its competitors (Figure 2).

The chart in Figure 3 shows that the company’s volume of mentions has been increasing ever since the acquisition occurred. However, a negative sentiment polarity has been steadily increasing due to social ESG risks related to collective health crises during COVID and security-disrupting threats. In addition, the company faced difficulties collecting rent from retailers leading to lawsuits.

The arrows in this chart indicate Unibail ESG risks in time. The first arrow points to the social risks generated by security threats, in 2016, and the second arrow points to the issue of unpaid rent and lawsuits filed regarding the matter, in 2020.

According to web data, Unibail has the second highest volume of sustainability mentions among analyzed groups. The company was notably related to sustainable development goals number 8* and number 12**. This volume is manifested in their initiatives to help unemployed people and maintain sustainable ethics and practices when launching their malls and shopping centers (Figure 4).

* Social development goal for decent work and economic growth.

** Social development goal for responsible consumption and production.

The impact of COVID on the emerging commercial real estate market

As previously mentioned, COVID had several effects on the industry, both negative and positive. Furthermore, it reshaped the market and its work policies. Some companies, as well, chose to switch to remote work and digitalization. In Figure 5, we can see that sentiment related to remote work policies has steadily improved since the pandemic started. However, in the last few months, we’ve seen a sharp decline, potentially signaling a negative reaction to some companies requiring employees back to their offices.

In addition, the pandemic has resulted in labor shortage and supply chain disruption, eventually leading to tremendous inflationary pressure. Raw materials prices, including oil, gas, iron, and wood, have witnessed a drastic increase and a disequilibrium between the volume of demand and the quantity available (Figure 6).

Data source

To produce this analysis, we combined natural language processing with billions of textual web data related to the real estate market, commercial real estate in particular. Using NLP-powered models gives us an edge as we can extract ESG, SDG, and financial insights that aren’t necessarily obvious or easy to detect. These insights help investors make better investment decisions.

SESAMm leverages artificial intelligence and machine learning to help you decipher and understand timely sentiments, trends, and ESG metrics on a wide range of public and private companies.

Stay in touch with SESAMm

Thanks for reading this issue of Alternative Data Trends. Be sure to catch the next issue by subscribing to our blog. And if you'd like a TextReveal® demo, send us a message via the form.

Inflation.

It's a word that most of us in the U.S. despise, almost as much as the word taxes. It's probably because, like taxes, we can't escape its wallet-draining effect when it increases. Maybe the way we feel about it is because the last time the U.S. economy deflated—giving us relief from it—was in the 1930s, when "Prices dropped an average of nearly 7% every year between the years of 1930 and 1933," according to Investopedia. But I digress.

We won’t go into how inflation works, but how the government calculates it—and how its categories affect it—has always been consistent. At least it was until the COVID-19 pandemic hit, that is.

What NLP text mining reveals about the U.S. economy inflation-rate factors and the online conversations about them

To ensure we're on the same page about how we came to the forthcoming information in this use case, let's cover a couple of basics on NLP text mining and inflation rate indexes.

What are NLP and text mining?

Natural language processing (NLP), an A.I. technology, automates the data analysis of mined textual, unstructured data. It includes natural language understanding and natural language generation to simulate a human’s ability to create language, and it’s a component of text mining that performs a special kind of linguistic analysis by deep learning algorithms so a machine can “read” text. Apps like Grammarly or Wordtune analyze text to improve a written text, for example, and chatbots use this technology to interact with customers.
Text mining, or text analytics, is the process of examining big data document collections. It’s a computer science discipline that converts unstructured text data in documents and databases into normalized, structured data and datasets for analysis by machine learning models. Deep learning machine-learning algorithms then analyze this data, analyzing semantics and grammatical structures, to gain new insight or aid research from human language.
Together, NLP and text mining are like a search engine on steroids.

The Consumer Price Index (CPI)

According to this Forbes Advisor article, "The two most frequently cited indexes that calculate the inflation rate in the U.S. are the Consumer Price Index (CPI) and the Personal Consumption Expenditures Price Index (PCE)." For this article, however, we'll only use the Bureau of Labor Statistics (BLS) method of CPI inflation calculation as a reference.
CPI observes a specific group of commonly-purchased goods and services to gauge how prices fluctuate. These foods and services include:

• Apparel: Women's and men's clothes, jewelry, etc.
• Alcoholic beverages: Beers, wine, liquor, etc.
• Energy and commodities: Gasoline, natural gas, electricity, etc.
• Food: Items bought by the average consumer, such as breakfast cereal, milk, meat, fruits, vegetables, etc.
• Housing and shelter: Rent, housing insurance, bedroom furniture, hotel or motel accommodation costs, etc.
• Medical care services: Physicians' services, prescription drugs, medical supplies, etc.
• New and used vehicles: Trucks, vans, sedans, SUVs, etc.
• Tobacco and smoking products: Tobacco-related items, such as cigarettes, cigars, bidis, kreteks, loose tobacco, etc.
• Transportation services: Airline fares, vehicle insurance, etc.

NLP text-mining process: web mentions matched to CPI categories

Using SESAMm's web text analysis engine TextReveal®, we analyzed textual data relating to the inflation topic within the U.S. from 2017 until now. For this analysis, we defined co-mentions as the articles and social media posts that mention "inflation" and at least one of the CPI categories. Note: Although we can analyze more than 100 languages, we focused on English in this case. Also, we didn’t conduct a sentiment analysis from the information extraction.

From 2017 to 2019, inflation co-mentions within the U.S. are relatively stable (see Figure 1). But this trend changes with the first shift in 2020, continuing its rapid growth and peak by the end of 2021 due to this surge of inflation reaching record levels.

What was one of the main drivers of the inflation surge? Used cars.

3 used-car and inflation trends uncovered through NLP Text Mining

According to the U.S. Bureau of Labor Statistics, the cost of used vehicles was one of the main drivers of the inflation spike. How did used cars contribute to inflation? The chain of events occurred like so: The increased used-car demand was fueled by a new-vehicle supply shortage caused by a chip shortage generated by supply-chain interruptions due to the COVID-19 pandemic.

As the pandemic-induced supply-chain interruption unfolded, used-car trends developed. Here are three we found in our data mining research:

Trend 1: Co-mentions percentage for used vehicles more than doubled

Based on the percentage of co-mentions compared to other topics, the used-car topic moves from the number eight spot to the number four spot in 2021 (see Figure 2).

Before 2020, mentions were relatively steady. However, we observe an increase in used-vehicles mentions caused by disruptions in supply chains leading to chip shortages (see Figure 3) as early as January 2020. These shortages led to a decrease in new vehicle inventory. The Statista report, indicating an increase of the used vehicle value index by 49 points compared to the price index recorded in 2020, supports our findings.

Trend 2: Used vehicle prices rose with used-car co-mentions

Because of the pandemic, car production nearly stopped along with the sale of cars, which created two situations: 1. high inventory to sales ratio and 2. historically low car production (see Figure 4). Vehicles sales picked up later, but car production was still suffering because of supply-chain disruption. That meant the inventory to sales ratio dropped to virtually zero.

So consumers with little-to-no options for new vehicles turned to used cars, increasing their demand and therefore increasing their prices. We confirm this hypothesis with increasing mentions within the used-vehicles topic, coinciding with an inventory volume decrease. All in all, used-vehicle prices rose 40.5%.

Trend 3: The COVID-19 pandemic and new vehicle inventory shortage increased demand

A smaller new-vehicle inventory wasn't the only reason consumers sought out used vehicles. They also wanted used cars because of the pandemic.

For 2020, we observe that consumers avoided public transportation by rising co-mentions between pandemic-related mentions and the demand for secondhand vehicles (see Figure 5).

Used-car and inflation trends summary

We can summarize the used-car and inflation trends with one phrase: It's a used-car seller's market. For example, online retailers like Carvana have leveraged these factors to grow significantly. In contrast, due mainly to significant supply chain disruptions, motor companies have had the opposite effect, with the Automotive industry projected to lose $210 Billion. Judging by the number of mentions in public web forums and social media, the chip shortage and used-car boom affected General Motors, Ford, and Toyota the most (see Figure 6).

About SESAMm and TextReveal’s® NLP Text-mining Capabilities

SESAMm is a leading company in alternative data and artificial intelligence, delivering global investment firms and corporations descriptive, prescriptive, or predictive investment analytics worldwide. TextReveal is SESAMm's premiere NLP text-mining product, a solution that allows you to fully leverage NLP-driven insights and receive high-quality results through data streams, modular API and dashboard visualization, and signals and alerts. In other words, we organize, categorize, and capture relevant information from raw data for you.

READ MORE: Check out "Alternative Data Trends – How Reddit Helped Fuel The Great Resignation."

Ready to uncover the invisible data about your investments and target companies? Request a TextReveal demo today.

SESAMm is very proud to announce it is the winner of the 2023 Fund Intelligence Operations and Services Awards for Best Data Provider, Alternative Data Sources.

A big thanks to SESAMm's investors, partners, and clients

We thank our clients, investors, and partners for your support and patronage. Thank you for being such a big part of SESAMm; you're why we do what we do, and many of you have been involved since day one. And your generous and encouraging attitude has helped get us here today.

About the award

This award is granted by a panel of leading industry experts based on our exceptional client service, innovative product development, and strong and sustainable business growth over the past 12 months.

Honored and excited

We're honored to earn Best Data Provider, Alternative Data Sources at 2023 Fund Intelligence Operations and Services Awards. We're also excited for our clients and partners because our products and services are game-changers for hedge fund services. And while we have more work to do and clients to serve, we think the future looks bright for us, our partners, and our clients.

About SESAMm and TextReveal

SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. Through TextReveal, we give you NLP capabilities to generate your own alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporation studies, and more. And with access to SESAMm’s massive data lake, made up of 20 billion articles and messages and growing, you can make better investment decisions.

Reach out to SESAMm

For a personal demonstration of our award-winning platform, reach out to a representative