Leasing Warehouse: Corporation Supply Chain Demand PostCovid

Posted on May 3, 2020
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

\leasing

As our nation recovers from quarantine, many industries are reevaluating their supply chains. Chains once thought to be highly optimized and streamlined to the global flow of goods. They have been crippled if not killed by the cascade of shutdowns instituted by municipal, state, and federal governments of nations all over the world. In this climate, the projections made last year about expected inventory or demand, or any other metric are out the window.

Corporations and their associated supply chains now float in stormy waters. The question then arises, what can be done? One of the best ways to adjust for uncertainty in supply chains is to catch a trend that's already been accelerating in industrial real estate for the past five years: leasing.

Leasing, a strategy Amazon pioneered, allows a company to be in more spaces faster and without an associated long term commitment. Flexibility is built-in. Resources can now be invested elsewhere, allowing a company to shift accordingly. But how does a company choose a good location? The costs associated with any given location are usually broken into three chunks:

  1. Cost of Storage (rent)
  2. Transport Costs (cost of getting good X from A to B, C, and D)
  3. Inventory Holding Costs (cost of unsold inventory)

Leasing DataΒ 

Absent detailed data on particular inventories and business models, I attempt to answer the first two with data I pulled from Loopnet.com.

Loopnet is a national listing site for commercial and industrial property. Listings are organized by city and state. The information on each page is submitted by realtors in a somewhat inconsistent fashion. As various important features of a property can be in different sections from listing to listing.

Each listing also has a section for nearby transport links - which usually consist of important supply chain hubs that a company would consider when leasing a property: nearby airports, railroads, freight ports, etc. I aggregated this information to assign a mean "connectivity" score for each city. Absent specific knowledge of a city's (or town's) traffic patterns, this gave me a good handle on how connected a given location was to the wider American trade network.

Here are six states with their connectivity scores for all cities:

Β leasingOverall, the connectivity metric proved to be the feature most correlated with the price per sq ft of the lease space. The predictive strength of the metric varied from state to state, with highest among New York, Florida, and Delaware and no correlation in Texas or Connecticut.

leasing

Leasing and Pricing

It goes without saying that national warehouse pricing isn't set or influenced by any single factor alone - but is a result of local demand, geography, and historical industrial developments. A good warehouse for an Iowa corn farmer doesn't need close access to several freight ports. But a warehouse in NYC does for companies intent on international distribution.

Absent any hard and fast rules, I created this visualization of the distribution of warehouses in the US, along with several averaged parameters that might aid in supply chain analysis.

leasing
These metrics,Β  correlations, and visualizations are offered as first steps and tools towards understanding - in a broad sense - the national and regional warehouse market.Β 

About Author

Sam Nuzbrokh

Sam Nuzbrokh is a certified data scientist with a Master's in Space Engineering and a Bachelors in Theoretical Physics. He has 3+ years of data science, engineering, and research experience across satellite communication, engineering telemetry, and academic research....
View all posts by Sam Nuzbrokh >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI