Urban Growth & Real Estate Value: Neighborhoods & Districts

Chapter: urban❓ Growth & Real Estate Value: Neighborhoods & Districts

Introduction

This chapter delves into the intricate relationship between urban growth and real estate value at the neighborhood and district levels. Understanding these dynamics is crucial for accurate real estate appraisal. We will explore how urban growth patterns influence property values, considering various scientific theories, practical applications, and relevant market factors.

1. City Origins and Growth Patterns:

• 1.1 Siting Factors:
  The initial location (siting) of a city significantly impacts its subsequent growth and land-use patterns. Cities often originated near:
  • Transportation Hubs: Seaports, river crossings, intersections of trade routes.
  • Power Sources: Waterfalls for mills, coal deposits for industry.
  • Strategic Locations: Defensive positions, commercially advantageous areas, political centers.
  • Natural Amenities: Climate, scenic beauty (leading to retirement communities or recreational resorts).
• 1.2 Expansion and Growth Drivers:
  From the initial site, a city expands outward based on factors such as:
  • Land Availability: The presence of developable land.
  • Technological Advancements: Building materials, transportation systems, communication networks.
  • Government Policies: Zoning regulations, infrastructure investment, provision of public services (water, sewage, roads).
• 1.3 Density and Development Corridors:
  As land becomes scarce, urban areas often experience increased density. Development corridors emerge, channeling new construction towards available land.

2. Models of Urban Growth

Several models attempt to describe and explain urban land-use patterns. It’s important to note that these models are simplifications of complex realities, and real-world cities often exhibit characteristics of multiple models.

• 2.1 Concentric Zone Theory (Burgess Model):
  • Developed in the 1920s, this model suggests that cities grow outward from a central business district (CBD) in a series of concentric zones.
  • Zones include:
    1. CBD: Central business district.
    2. Transition Zone: Area of mixed residential and commercial uses, often characterized by deterioration.
    3. Working-Class Homes: Modest housing for factory workers.
    4. Residential Zone: Higher-quality housing.
    5. Commuter Zone: Suburban areas.
  • Limitations: This model is less applicable to modern cities with multiple centers and complex transportation networks.
• 2.2 Sector Theory❓❓ (Hoyt Model):
  • Developed in the 1930s, this model suggests that cities grow in wedge-shaped sectors radiating outward from the CBD along transportation routes.
  • Different types of land uses (e.g., residential, industrial) tend to concentrate in particular sectors.
  • Limitations: Overemphasis on transportation and doesn’t fully account for the development of multiple nuclei.
• 2.3 Multiple Nuclei Theory (Harris and Ullman Model):
  • Developed in the 1940s, this model suggests that cities develop around multiple centers or nuclei, each with its specialized function.
  • These nuclei may include: CBD, industrial parks, shopping centers, residential areas.
  • The growth and interaction of these nuclei shape the urban landscape.
  • Strengths: More realistic representation of modern, decentralized cities.
• 2.4 Radial (Axial) Corridor Theory:
  • This theory combines elements of the sector and multiple nuclei models. It suggests growth occurs along major transportation routes extending from the city center.

3. Transportation and Urban Growth:

• 3.1 Impact of Transportation Networks: Transportation improvements (roads, railways, public transit) greatly influence urban growth. Improved access allows cities to expand and serve larger markets.
• 3.2 Radial Growth: Growth often radiates outward from the CBD along major transportation routes.
• 3.3 Freeway Systems: Major freeway systems can lead to decentralization, with businesses and residents migrating from the city core.

4. Real Estate Districts: Characteristics and Value Influences:

The characteristics and value influences vary depending on the type of district. The four forces influencing all real estate demand (utility, scarcity, desire, and effective purchasing power) affect districts, but their relative importance shifts.

• 4.1 One-Unit Residential Districts:

  • Defining Characteristic: Predominance of owner-occupied single-family homes.
  • Examples: Custom-built subdivisions, attached housing (condominiums, townhouses), senior housing, rural housing.
  • Value Influences:
    1. Access to Workplaces: Proximity and commuting time.
    2. Transportation Service: Availability of public transit, road network.
    3. Access to Amenities: Shopping centers, cultural facilities, schools.
    4. Reputation of Area: Safety, quality of life.
    5. Residential Atmosphere: Appearance, landscaping, noise levels.
    6. Protection from Intrusion: Buffers from commercial or industrial areas.
    7. Proximity to Natural Features: Parks, lakes, rivers.
    8. Land Use Restrictions: Zoning, covenants, conditions, and restrictions (CC&Rs).
    9. Community spirit, which is evidenced in activities such as block parties and street fairs, and activist efforts, such as lobbying against undesirable rezoning or development, can make a residential area more stable or even reverse a trend toward declining property values.

• 4.2 Multifamily Residential Districts:

  • Defining Characteristic: High density, predominance of rental units or privately owned apartments (cooperatives, condominiums).
  • Examples: High-rise buildings, garden apartments, townhouses, mixed-use buildings.
  • Value Influences:
    1. Access to Workplaces and Amenities: Similar to single-family districts.
    2. Reputation of Area: Safety, quality of life.
    3. Residential Atmosphere: Appearance, landscaping, noise levels, maintenance.
    4. Protection from Intrusion: Buffers from commercial or industrial areas.
    5. Parking: Adequate parking for tenants and guests.
    6. Vacancy and Turnover Rates: Indicators of demand.
    7. Proximity to Public Transportation
    8. School District
    9. Foreclosures and Short Sales

• 4.3 Commercial Districts:

  • Defining Characteristic: Concentration of businesses and retail establishments.
  • Examples: Highway commercial strips, retail districts (shopping centers), downtown central business districts (CBDs).
  • Value Influences:
    1. Trade Area: The geographic area served by the businesses.
    2. Purchasing Power: Income levels, demographics of the trade area.
    3. Accessibility: Road network, public transit.
    4. Visibility: Prominent location, signage.
    5. Competition: Presence of similar businesses.
    6. Traffic Counts: Pedestrian and vehicular traffic.
    7. Availability of Parking
    8. Vacancy and Rental Rates
    9. Inventory levels

• 4.4 Office Districts:

  • Defining Characteristic: Concentration of office buildings and related services.
  • Examples: Central business districts, suburban office parks, office condominiums.
  • Value Influences:
    1. Accessibility: Proximity to labor force, transportation networks.
    2. Building Configuration: Floorplate size, ceiling height.
    3. Physical Characteristics: Visibility, attractiveness, quality of construction.
    4. Direction of Growth: Emerging areas vs. declining areas.
    5. Competition: Presence of other office buildings.
    6. Availability of Land for Expansion
    7. Pedestrian or Vehicular Traffic Count
    8. Vacancy and Rental Rates

• 4.5 Retail Districts:

  • Defining Characteristic: Concentration of competing retail locations
  • Examples: Regional shopping centers, community shopping centers, neighborhood shopping centers, specialty centers.
  • Value Influences:
    1. Purchasing Power: Income levels, demographics of the trade area.
    2. Significant Locational Considerations such as the time-distance from potential customers, access, highway medians, and traffic signals
    3. Physical Characteristics such as the visibility, attractiveness, quality of construction, and condition of properties
    4. Direction of Observable Growth
    5. Character and Location of Existing or Anticipated Competition
    6. Retailers’ inventory
    7. Availability of Land for Expansion and Customer Parking
    8. Pedestrian or Vehicular Traffic Count
    9. Location within a city or town or proximity to anchors and core groupings
    10. Vacancy and Rental Rates

5. Analyzing Market Participant’s Ideas and Preferences:

• Comparable Sales Analysis: Analyze comparable sales to understand what makes an area desirable to market participants. Compare sale prices of similar properties in different locations to determine dollar and percentage differences reflecting locational advantages or disadvantages.

6. Mathematical Considerations and Modeling:

• 6.1 Hedonic Pricing Models:

  • Used to estimate the contribution of individual property characteristics (including location) to overall property value.
  • General Form:

  P = f(S, N, L, E)

  Where:
  * P = Property price
  * S = Structural attributes (size, age, number of bedrooms, etc.)
  * N = Neighborhood characteristics❓❓ (school quality, crime rates, amenities)
  * L = Locational attributes (proximity to CBD, transportation, natural features)
  * E = Externalities (environmental factors, pollution)

• 6.2 Regression Analysis: Regression analysis can be used to estimate the coefficients in a hedonic pricing model, quantifying the impact of each variable on property value.

• 6.3 Spatial Statistics:

  • Methods such as spatial autocorrelation (e.g., Moran’s I) can be used to analyze the degree to which property values are clustered spatially, indicating the influence of neighborhood effects.

7. Practical Application and Experimentation:

• 7.1 Case Study: Analyzing the Impact of a New Transit Line:

  • Hypothesis: A new transit line will increase property values in residential areas within a certain radius of transit stations.
  • Data Collection: Collect data on property sales prices, distances to transit stations, and other relevant variables (e.g., property characteristics, neighborhood demographics).
  • Analysis: Use regression analysis to determine the impact of proximity to transit stations on property values, controlling for other variables.
  • Expected Results: Properties closer to transit stations will show a statistically significant increase in value.

• 7.2 Experiment: Survey of Homebuyers Regarding Desirable Neighborhood Attributes:

  • Methodology: Conduct a survey of recent homebuyers in a specific urban area. Ask them to rank the importance of various neighborhood attributes (e.g., safety, school quality, proximity to parks, access to transportation, walkability).
  • Analysis: Analyze the survey data to identify the most highly valued neighborhood attributes.
  • Application: Use the findings to understand the factors driving demand in different neighborhoods and to inform appraisal analyses.

8. telecommuting❓ Impact on Residential Districts
* In recent years, the growth of the telecommuting workforce has made rural and exurban areas far from urban and suburban employment centers into feasible residential communities by eliminating the commute. On a national level, the combination of telecommuters and self-employed individuals working at home makes up a small percentage of the total workforce, but, according to a 2017 report, the number of home workers increased 115% between 2005 and 2017. The influence of telecommuting on the supply of and demand for housing in a residential district can be significant, depending on the demographics of the area.

9. Conclusion:

Understanding the complex interplay between urban growth and real estate value at the neighborhood and district levels is essential for sound real estate appraisal. By applying relevant scientific theories, analyzing market data, and considering the specific characteristics of each district, appraisers can develop accurate and reliable value estimates.