This line chart illustrates the average price per square foot across New York City's boroughs from 2018 to 2023. Each line represents one borough, showing variations in pricing trends over quarters. Manhattan (red line) consistently exhibits the highest price per square foot, reflecting its position as the city's premium real estate market. By contrast, boroughs such as Staten Island (green line) display relatively stable and lower price trends, indicative of their more affordable housing options. Notable fluctuations, such as spikes in Manhattan and Queens (light teal line), may signify shifts in market demand, economic conditions, or property development patterns. The interactive features are similar with the figure 5, enhancing this visual’s analytical values.

To capture these geographic dynamics, I created a geospatial map illustrating the average total sales price at the ZIP-code level from 2003 to 2023. Each ZIP code is shaded in varying intensities of red, with darker shades representing areas with higher average sales prices. The map provides a granular view of geographic disparities in property prices, highlighting high-value neighborhoods such as those in Manhattan and parts of Brooklyn while showing more affordable areas in the Bronx and Staten Island. To enhance the functionality of this map, I added the hover-over tooltips, zip code, to reveal the details information, such as the specific zip code and its corresponding average sales price. Users can also refine the map by selecting specific property types using the “building class” filter or adjusting the timeframe using the “sale date” filter. For instance, narrowing the view to a single year like 2022 enables a more focused analysis of recent market conditions. Additionally, the interactive map allows zooming and panning, helping users explore spatial patterns in detail and navigate between borough seamlessly.

Building on the spatial insights from the geo map, I created a tree map to visualize the count of property sales across neighborhoods in NYC from 2003 to 2023. Each block corresponds to a neighborhood, with its size and color intensity reflecting the number of sales. Larger and darker blocks indicate neighborhoods with higher property sale counts, such as Long Island City in Queens or the Upper East Side in Manhattan. Smaller and lighter blocks represent neighborhoods with fewer transactions. I believe this tree map segments sales data by borough and neighborhood, making it easy to identify high-activity areas within each borough. This tree map is particularly effective for exploring the distribution of property sales within NYC at a neighborhood level. It provides a clear understanding of which areas have been most active in the real estate market.

To further refine the analysis, I created a bar chart comparing the average sales prices across different property classes in NYC. Each bar’s height corresponds to the relative average sale price within a property class, and the color intensity emphasizes the higher-priced classes. The chart highlights the hierarchy of property types based on their average sale price. For instance, "Office Buildings" and "Condominiums" are among the highest-priced categories, while property types like "Two-Family Dwellings" or "Vacant Land" rank lower. This ranking offers insight into the premium property types in NYC’s real estate market. Moreover, the visualization illustrates the diversity of property classes, emphasizing how certain categories dominate the market in terms of value. For example, the high average prices of office buildings reflect NYC’s status as a global business hub. Lower-priced property classes, such as "Vacant Land" and "Industrial Buildings," could indicate opportunities for developers or stakeholders seeking affordable investment options.

From an investor's perspective, understanding where value lies within New York City's real estate market is paramount. To uncover such opportunities, I created a geospatial map that highlights investment opportunities in New York City by identifying neighborhoods classified as undervalued based on their average price per square foot. The undervalued designation applies to areas where the PPSF is significantly lower than the borough's mean PPSF for a specific year. The neighborhoods are color-coded for clarity: Orange represents undervalued regions, while blue indicates neighborhoods that are not classified as undervalued. The map reveals clusters of undervalued neighborhoods, which may be located in less developed or transitioning areas. These regions could represent growth potential, especially for developers and investors seeking higher returns in the future. Neighborhoods not classified as undervalued are likely to have PPSF values in line with or above the borough average. These regions may already be established or premium real estate markets. The map enables users to zoom in on specific boroughs or neighborhoods, facilitating a closer look at potential hotspots for investment.

This visualization is particularly valuable for assessing market inefficiencies and identifying growth potential in NYC's real estate landscape. By contrasting neighborhood PPSF values with borough-wide averages, stakeholders gain a nuanced understanding of where market conditions may not yet reflect true value. Additionally, tracking changes in undervalued statuses over time can inform long-term investment strategies and urban development policies.

In addition to the property sales data, I developed multiple visualizations to demonstrate the demographic in several relevant metrics across the boroughs. In Figure 11, This line chart displays the average household income trends across NYC boroughs from 2019 to 2023. Each line represents one of the boroughs, providing a comparative view of income growth over the years.

Manhattan (Red Line) consistently has the highest average household income, reflecting its status as the financial and cultural hub of NYC. Over the analyzed years, Manhattan shows steady income growth, widening the gap with other boroughs. Staten Island (Green Line) and Queens (Orange Line) demonstrate moderate income levels, with consistent growth trajectories suggesting stable economic conditions. Brooklyn (Yellow Line) and The Bronx (Blue Line) exhibit lower average household incomes compared to other boroughs, with slower but steady increases over time, indicating gradual economic improvement in these areas. Income levels across all boroughs show an upward trend from 2019 to 2023, which may reflect broader economic recovery efforts following the COVID-19 pandemic.

This visualization can help different audience groups align their strategies with regional economic conditions. For Policymakers, the consistent gap between Manhattan and other boroughs highlights income inequality, suggesting a need for targeted policies to support economic growth in underperforming areas like the Bronx and Brooklyn. While for Investors, boroughs with steadily growing incomes, such as Queens and Staten Island, may indicate emerging markets with potential for increased housing demand and property appreciation. When it comes to developers, understanding income growth trends is critical for tailoring housing and commercial projects to the economic realities of specific boroughs. For instance, higher-income regions like Manhattan may support luxury developments, while other areas might benefit from affordable housing initiatives.

Besides household income landscape, I developed three bubble chart and stacked bar chart to showcase the population distribution by race, by education, and by age groups across the boroughs. These visualizations serve as valuable tool for exploring the intersection of population, education, age, and geography in NYC. It offers stakeholders actionable insights for addressing educational disparities and aligning resources with borough-specific needs. Paired with additional socioeconomic data, such as race and age group, this visualization can provide a comprehensive view of NYC’s demographic landscape.

To complement the analysis, I developed three bubble chart and stacked bar chart to showcase the population distribution by race, by education, and by age groups across the boroughs.