Property Address City, State, Zip Selling Price

Sheet1propertyaddresscitystatezipselling Price000ambertechnical Trai

Analyze the provided dataset containing property addresses, cities, states, ZIP codes, selling prices, and additional metadata such as training and technical training labels. The dataset includes diverse property types such as hospitals, warehouses, medical centers, educational facilities, office buildings, and industrial units across various states in the United States. The objective is to perform a comprehensive assessment of the dataset, focusing on data quality, key patterns, and insights related to property valuation, geographic distribution, and investment potential.

Paper For Above instruction

The dataset provided encompasses a broad spectrum of commercial real estate properties across the United States, with information on location, sale price, and additional attributes. Analyzing such data requires multiple steps, including data cleaning, descriptive statistics, geographic analysis, and valuation assessment, integrated within a structured methodology.

Firstly, data cleaning is critical to ensure consistency. The dataset appears to contain duplications, inconsistent formatting, and some entries with placeholder or missing values. For instance, the 'selling price' column shows entries such as `$0.00`, which need to be verified for accuracy—either as data entry errors or meaningful representations of properties not yet sold or not valued. Also, some properties have extremely high sale prices, such as $88,489,000, indicating high-value medical centers or hospitals, while others are in the low thousands, representing smaller warehouses or office spaces.

Next, descriptive analysis provides insight into the typical property value range. The median price seems to cluster around several million dollars, with certain properties valued over $80 million, likely being medical centers or large hospital complexes. The dataset suggests a skewed distribution characterized by high-priced institutional properties and lower-priced commercial units. A statistical overview using measures like mean, median, mode, and standard deviation can reveal the overall valuation landscape.

Geographic distribution analysis helps reveal regional trends. States like California, Texas, and Illinois have multiple entries with substantial variation in prices. For example, California properties like the Casa Mesa Regional Medical Center in Inglewood and Deaconness Medical Center in Alameda are high-value assets, indicating lucrative markets for healthcare real estate. Meanwhile, properties in smaller states or cities such as Brookfield, WI, and Salem, NH, tend to have more modest sale prices, which reflects local market conditions and economic factors.

Investigation of the relationship between property type and valuation is also insightful. Medical centers and hospitals dominate high-value entries, implying healthcare infrastructure as a key driver for property valuation. Warehouses and educational facilities are generally valued lower but still show significant regional variation. For example, the Ardenna Wood Medical Center and Belmar Group Warehouse exemplify this trend.

Furthermore, the temporal context—although not explicitly provided—could be integrated if additional data such as acquisition dates were available. Given the mention of a term of loan, interest rate, and monthly payments, there is an inference that these properties are financed investments. Analyzing the finance terms in conjunction with property valuation could reveal market lending trends, risk assessments, and investment attractiveness. For instance, properties with high valuations might attract higher loan amounts, influencing the existing market interest rates and financing strategies.

The overall valuation pattern aligns with known market trends. Healthcare properties tend to be stable investments with high capital requirements but consistent returns, especially in urban regions with population growth. Warehousing and industrial properties, increasingly critical due to e-commerce expansion, show promising growth potentials, particularly in logistics hubs like those in Illinois or Utah, as indicated by the properties in locations like Brighton and Pocatello.

In conclusion, the dataset illustrates diverse property types characterized by significant regional and valuation disparities. Analytical insights suggest that healthcare properties constitute the most valuable assets, with high market stability and continued demand, especially in urban centers. There is also a noticeable trend towards industrial and warehouse properties supporting logistics and distribution, aligning with current economic shifts. Future analysis could benefit from integrating temporal data, rental income metrics, and occupancy rates to deepen investment analysis and property valuation accuracy. Addressing data inconsistencies and expanding the dataset with transaction histories would further enhance these insights.

References

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• Real Capital Analytics. (2023). Commercial Property Price Index. https://www.rcanalytics.com