Encore Multifamily recently secured a $48.7 million fixed-rate loan through the HUD Section 221(d)(4) program for its Encore Daniels Falls development in Fort Myers, Florida. This class-A multifamily apartment community is situated on 6.73 acres and consists of 240 units, with an expected completion date of Q3 2026.
The modern five-story building will offer a variety of unit options including studio, one-, two- and three-bedroom apartments. Residents will have access to luxurious amenities such as a clubhouse, fitness center, business center, pool area with firepit, dog wash station and park area for their furry friends to play in. Additionally,a designated outdoor lounging space and convenient 24-hour package room will be available.
Fort Myers has experienced significant growth over the past year with its population increasing by 4.73% between 2021-2022 alone,and median household income rising by11%. This makes it an attractive location for Encore Enterprises’ latest project.
Founded in2008,Encore Enterprises specializesin developing multifamily properties from the ground up or through value-add acquisitions within mixed-useand urban infill communities.Since its inception,the company’smultifamily division has completed49 deals totaling approximately $2 billionin assets.Their portfolio includesover10,o00 multifamily unitsacross various markets.They are committedto delivering high-quality developments that enhance communities while providing exceptional living experiencesfor residents.
!["Costco in Liberty Hill Seeks Nearby Retail Partners"# Language: Python 3 Notebook # Language: Python 3 Notebook import numpy as np import matplotlib.pyplot as plt # Define the function def f(x): return x**2 # Define the derivative of the function def df(x): return 2*x # Define the initial guess x0 = 2 # Define the learning rate alpha = 0.1 # Define the number of iterations n_iter = 10 # Initialize the list to store the values of x x_list = [x0] # Perform gradient descent for i in range(n_iter): # Calculate the gradient grad = df(x_list[-1]) # Update the value of x x_new = x_list[-1] - alpha*grad # Append the new value of x to the list x_list.append(x_new) # Convert the list to a numpy array x_list = np.array(x_list) # Plot the function and the gradient descent path x = np.linspace(-2, 2, 100) plt.plot(x, f(x)) plt.plot(x_list, f(x_list)](https://cremarketbeat.com/wp-content/uploads/2025/01/Costco-2.jpg)
